MERGE EVERYTHING¶

Zhaoning Wang 2023-11-13¶

In [3]:
library(Seurat)
library(Signac)
library(ggplot2)
library(ggrepel)
library(ggridges)

library(cowplot)
library(dplyr)
library(RColorBrewer)
library(S4Vectors)
library(sctransform)
Error in library(Signac): there is no package called ‘Signac’
Traceback:

1. library(Signac)
In [4]:
installed.packages()
A matrix: 330 × 16 of type chr
PackageLibPathVersionPriorityDependsImportsLinkingToSuggestsEnhancesLicenseLicense_is_FOSSLicense_restricts_useOS_typeMD5sumNeedsCompilationBuilt
abindabind /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.4-5 NA R (>= 1.5.0) methods, utils NA NA NA LGPL (>= 2) NANANANAno 4.3.2
AnnotationDbiAnnotationDbi /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.64.1 NA R (>= 2.7.0), methods, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST NA utils, hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr NA Artistic-2.0 NANANANAno 4.3.2
AnnotationFilterAnnotationFilter/projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.26.0 NA R (>= 3.4.0) utils, methods, GenomicRanges, lazyeval NA BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown NA Artistic-2.0 NANANANAno 4.3.2
askpassaskpass /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.2.0 NA NA sys (>= 2.1) NA testthat NA MIT + file LICENSENANANANAyes4.3.1
assertthatassertthat /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.2.1 NA NA tools NA testthat, covr NA GPL-3 NANANANAno 4.3.0
backportsbackports /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.4.1 NA R (>= 3.0.0) NA NA NA NA GPL-2 | GPL-3 NANANANAyes4.3.0
basebase /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library4.3.2 base NA NA NA methods NA Part of R 4.3.2 NANANANANA 4.3.2
base64encbase64enc /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.1-3 NA R (>= 2.9.0) NA NA NA png GPL-2 | GPL-3 NANANANAyes4.3.0
batchelorbatchelor /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.18.0 NA SingleCellExperiment SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, DelayedArray, DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmatRcpp testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq NA GPL-3 NANANANAyes4.3.2
beachmatbeachmat /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.18.0 NA NA methods, DelayedArray (>= 0.27.2), SparseArray, BiocGenerics, Matrix, Rcpp Rcpp testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array NA GPL-3 NANANANAyes4.3.2
beeswarmbeeswarm /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.4.0 NA NA stats, graphics, grDevices, utils NA NA NA Artistic-2.0 NANANANAyes4.3.2
BHBH /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.81.0-1NA NA NA NA NA NA BSL-1.0 NANANANAno 4.3.0
BiobaseBiobase /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.62.0 NA R (>= 2.10), BiocGenerics (>= 0.27.1), utils methods NA tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr NA Artistic-2.0 NANANANAyes4.3.2
BiocFileCacheBiocFileCache /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.10.1 NA R (>= 3.4.0), dbplyr (>= 1.0.0) methods, stats, utils, dplyr, RSQLite, DBI, filelock, curl, httr NA testthat, knitr, BiocStyle, rmarkdown, rtracklayer NA Artistic-2.0 NANANANAno 4.3.2
BiocGenericsBiocGenerics /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.48.1 NA R (>= 4.0.0), methods, utils, graphics, stats methods, utils, graphics, stats NA Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit NA Artistic-2.0 NANANANAno 4.3.2
BiocIOBiocIO /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.12.0 NA R (>= 4.3.0) BiocGenerics, S4Vectors, methods, tools NA testthat, knitr, rmarkdown, BiocStyle NA Artistic-2.0 NANANANAno 4.3.2
BiocManagerBiocManager /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.30.22 NA NA utils NA BiocVersion, remotes, rmarkdown, testthat, withr, curl, knitr NA Artistic-2.0 NANANANAno 4.3.2
BiocNeighborsBiocNeighbors /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.20.0 NA NA Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix Rcpp, RcppHNSW testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW NA GPL-3 NANANANAyes4.3.2
BiocParallelBiocParallel /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.36.0 NA methods, R (>= 3.5.0) stats, utils, futile.logger, parallel, snow, codetools BH, cpp11 BiocGenerics, tools, foreach, BBmisc, doParallel, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr, batchtools, data.table RmpiGPL-2 | GPL-3 NANANANAyes4.3.2
BiocSingularBiocSingular /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.18.0 NA NA BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, ScaledMatrix, irlba, rsvd, Rcpp, beachmat Rcpp, beachmat testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix NA GPL-3 NANANANAyes4.3.2
BiocVersionBiocVersion /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library3.18.0 NA R (>= 4.3.0) NA NA NA NA Artistic-2.0 NANANANAno 4.3.2
biomaRtbiomaRt /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.58.0 NA methods utils, XML (>= 3.99-0.7), AnnotationDbi, progress, stringr, httr, digest, BiocFileCache, rappdirs, xml2 NA BiocStyle, knitr, mockery, rmarkdown, testthat, webmockr NA Artistic-2.0 NANANANAno 4.3.2
BiostringsBiostrings /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.70.1 NA R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.31.2), XVector (>= 0.37.1), GenomeInfoDbmethods, utils, grDevices, graphics, stats, crayon S4Vectors, IRanges, XVectorBSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit, BiocStyle, knitr RmpiArtistic-2.0 NANANANAyes4.3.2
bitbit /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library4.0.5 NA R (>= 2.9.2) NA NA testthat (>= 0.11.0), roxygen2, knitr, rmarkdown, microbenchmark, bit64 (>= 4.0.0), ff (>= 4.0.0) NA GPL-2 | GPL-3 NANANANAyes4.3.0
bit64bit64 /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library4.0.5 NA R (>= 3.0.1), bit (>= 4.0.0), utils, methods, stats NA NA NA NA GPL-2 | GPL-3 NANANANAyes4.3.0
bitopsbitops /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.0-7 NA NA NA NA NA NA GPL (>= 2) NANANANAyes4.3.0
blobblob /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.2.4 NA NA methods, rlang, vctrs (>= 0.2.1) NA covr, crayon, pillar (>= 1.2.1), testthat NA MIT + file LICENSENANANANAno 4.3.0
bootboot /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.3-28.1recommendedR (>= 3.0.0), graphics, stats NA NA MASS, survival NA Unlimited NANANANAno 4.3.0
BPCellsBPCells /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.1.0 NA R (>= 2.10) methods, grDevices, magrittr, Matrix, Rcpp, rlang, vctrs, stringr, tibble, dplyr, tidyr, ggplot2, scales, patchwork, scattermore, ggrepel, RColorBrewer, hexbin Rcpp, RcppEigen IRanges, GenomicRanges, matrixStats NA MIT NANANANAyes4.3.2
broombroom /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.0.5 NA R (>= 3.5) backports, dplyr (>= 1.0.0), ellipsis, generics (>= 0.0.2), glue, lifecycle, purrr, rlang, stringr, tibble (>= 3.0.0), tidyr (>= 1.0.0) NA AER, AUC, bbmle, betareg, biglm, binGroup, boot, btergm (>= 1.10.6), car, carData, caret, cluster, cmprsk, coda, covr, drc, e1071, emmeans, epiR, ergm (>= 3.10.4), fixest (>= 0.9.0), gam (>= 1.15), gee, geepack, ggplot2, glmnet, glmnetUtils, gmm, Hmisc, irlba, interp, joineRML, Kendall, knitr, ks, Lahman, lavaan, leaps, lfe, lm.beta, lme4, lmodel2, lmtest (>= 0.9.38), lsmeans, maps, margins, MASS, mclust, mediation, metafor, mfx, mgcv, mlogit, modeldata, modeltests, muhaz, multcomp, network, nnet, orcutt (>= 2.2), ordinal, plm, poLCA, psych, quantreg, rmarkdown, robust, robustbase, rsample, sandwich, sp, spdep (>= 1.1), spatialreg, speedglm, spelling, survey, survival, systemfit, testthat (>= 2.1.0), tseries, vars, zooNA MIT + file LICENSENANANANAno 4.3.0
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
tibbletibble /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library3.2.1 NA R (>= 3.4.0) fansi (>= 0.4.0), lifecycle (>= 1.0.0), magrittr, methods, pillar (>= 1.8.1), pkgconfig, rlang (>= 1.0.2), utils, vctrs (>= 0.4.2) NA bench, bit64, blob, brio, callr, cli, covr, crayon (>= 1.3.4), DiagrammeR, dplyr, evaluate, formattable, ggplot2, here, hms, htmltools, knitr, lubridate, mockr, nycflights13, pkgbuild, pkgload, purrr, rmarkdown, stringi, testthat (>= 3.0.2), tidyr, withrNA MIT + file LICENSE NANANANAyes4.3.0
tidyrtidyr /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.3.0 NA R (>= 3.4.0) cli (>= 3.4.1), dplyr (>= 1.0.10), glue, lifecycle (>= 1.0.3), magrittr, purrr (>= 1.0.1), rlang (>= 1.0.4), stringr (>= 1.5.0), tibble (>= 2.1.1), tidyselect (>= 1.2.0), utils, vctrs (>= 0.5.2) cpp11 (>= 0.4.0) covr, data.table, knitr, readr, repurrrsive (>= 1.1.0), rmarkdown, testthat (>= 3.0.0) NA MIT + file LICENSE NANANANAyes4.3.0
tidyselecttidyselect /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.2.0 NA R (>= 3.4) cli (>= 3.3.0), glue (>= 1.3.0), lifecycle (>= 1.0.3), rlang (>= 1.0.4), vctrs (>= 0.4.1), withr NA covr, crayon, dplyr, knitr, magrittr, rmarkdown, stringr, testthat (>= 3.1.1), tibble (>= 2.1.3) NA MIT + file LICENSE NANANANAno 4.3.0
tidyversetidyverse /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.0.0 NA R (>= 3.3) broom (>= 1.0.3), conflicted (>= 1.2.0), cli (>= 3.6.0), dbplyr (>= 2.3.0), dplyr (>= 1.1.0), dtplyr (>= 1.2.2), forcats (>= 1.0.0), ggplot2 (>= 3.4.1), googledrive (>= 2.0.0), googlesheets4 (>= 1.0.1), haven (>= 2.5.1), hms (>= 1.1.2), httr (>= 1.4.4), jsonlite (>= 1.8.4), lubridate (>= 1.9.2), magrittr (>= 2.0.3), modelr (>= 0.1.10), pillar (>= 1.8.1), purrr (>= 1.0.1), ragg (>= 1.2.5), readr (>= 2.1.4), readxl (>= 1.4.2), reprex (>= 2.0.2), rlang (>= 1.0.6), rstudioapi (>= 0.14), rvest (>= 1.0.3), stringr (>= 1.5.0), tibble (>= 3.1.8), tidyr (>= 1.3.0), xml2 (>= 1.3.3)NA covr (>= 3.6.1), feather (>= 0.3.5), glue (>= 1.6.2), mockr (>= 0.2.0), knitr (>= 1.41), rmarkdown (>= 2.20), testthat (>= 3.1.6) NA MIT + file LICENSE NANANANAno 4.3.0
timechangetimechange /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.2.0 NA R (>= 3.3) NA cpp11 (>= 0.2.7) testthat (>= 0.7.1.99), knitr NA GPL-3 NANANANAyes4.3.0
timeDatetimeDate /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library4022.108 NA R (>= 3.6.0) graphics, utils, stats, methods NA RUnit NA GPL (>= 2) NANANANAno 4.3.0
tinytextinytex /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.48 NA NA xfun (>= 0.29) NA testit, rstudioapi NA MIT + file LICENSE NANANANAno 4.3.1
toolstools /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library4.3.2 baseNA NA NA codetools, methods, xml2, curl, commonmark, knitr, xfun, mathjaxr, V8 NA Part of R 4.3.2 NANANANAyes4.3.2
triebeardtriebeard /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.4.1 NA NA Rcpp Rcpp knitr, rmarkdown, testthat NA MIT + file LICENSE NANANANAyes4.3.0
TTRTTR /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.24.3 NA NA xts (>= 0.10-0), zoo, curl xts RUnit quantmodGPL (>= 2) NANANANAyes4.3.0
tzdbtzdb /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.4.0 NA R (>= 3.5.0) NA cpp11 (>= 0.4.2) covr, testthat (>= 3.0.0) NA MIT + file LICENSE NANANANAyes4.3.0
urltoolsurltools /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.7.3 NA R (>= 2.10) Rcpp, methods, triebeard Rcpp testthat, knitr NA MIT + file LICENSE NANANANAyes4.3.0
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uwotuwot /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.1.16 NA Matrix Rcpp, methods, FNN, RcppAnnoy (>= 0.0.17), irlba Rcpp, RcppProgress, RcppAnnoy, dqrng testthat, covr, bigstatsr, RSpectra NA GPL (>= 3) NANANANAyes4.3.0
vctrsvctrs /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.6.4 NA R (>= 3.5.0) cli (>= 3.4.0), glue, lifecycle (>= 1.0.3), rlang (>= 1.1.0) NA bit64, covr, crayon, dplyr (>= 0.8.5), generics, knitr, pillar (>= 1.4.4), pkgdown (>= 2.0.1), rmarkdown, testthat (>= 3.0.0), tibble (>= 3.1.3), waldo (>= 0.2.0), withr, xml2, zeallot NA MIT + file LICENSE NANANANAyes4.3.1
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viridisLiteviridisLite/projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.4.2 NA R (>= 2.10) NA NA hexbin (>= 1.27.0), ggplot2 (>= 1.0.1), testthat, covr NA MIT + file LICENSE NANANANAno 4.3.0
vroomvroom /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.6.4 NA R (>= 3.6) bit64, cli (>= 3.2.0), crayon, glue, hms, lifecycle (>= 1.0.3), methods, rlang (>= 0.4.2), stats, tibble (>= 2.0.0), tidyselect, tzdb (>= 0.1.1), vctrs (>= 0.2.0), withr cpp11 (>= 0.2.0), progress (>= 1.2.1), tzdb (>= 0.1.1)archive, bench (>= 1.1.0), covr, curl, dplyr, forcats, fs, ggplot2, knitr, patchwork, prettyunits, purrr, rmarkdown, rstudioapi, scales, spelling, testthat (>= 2.1.0), tidyr, utils, waldo, xml2 NA MIT + file LICENSE NANANANAyes4.3.1
withrwithr /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.5.2 NA R (>= 3.2.0) graphics, grDevices, stats NA callr, covr, DBI, knitr, lattice, methods, rlang, rmarkdown (>= 2.12), RSQLite, testthat (>= 3.0.0) NA MIT + file LICENSE NANANANAno 4.3.1
xfunxfun /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.41 NA NA stats, tools NA testit, parallel, codetools, rstudioapi, tinytex (>= 0.30), mime, markdown (>= 1.5), knitr (>= 1.42), htmltools, remotes, pak, rhub, renv, curl, jsonlite, magick, yaml, rmarkdown NA MIT + file LICENSE NANANANAyes4.3.1
XMLXML /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library3.99-0.15NA R (>= 4.0.0), methods, utils NA NA bitops, RCurl NA BSD_3_clause + file LICENSE NANANANAyes4.3.2
xml2xml2 /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.3.5 NA R (>= 3.1.0) methods NA covr, curl, httr, knitr, magrittr, mockery, rmarkdown, testthat (>= 2.1.0) NA MIT + file LICENSE NANANANAyes4.3.0
xtablextable /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.8-4 NA R (>= 2.10.0) stats, utils NA knitr, plm, zoo, survival NA GPL (>= 2) NANANANAno 4.3.0
xtsxts /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.13.1 NA R (>= 3.6.0), zoo (>= 1.7-12) methods zoo timeSeries, timeDate, tseries, chron, tinytest NA GPL (>= 2) NANANANAyes4.3.0
XVectorXVector /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library0.42.0 NA R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9)methods, utils, tools, zlibbioc, BiocGenerics, S4Vectors, IRanges S4Vectors, IRanges Biostrings, drosophila2probe, RUnit NA Artistic-2.0 NANANANAyes4.3.2
yamlyaml /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library2.3.7 NA NA NA NA RUnit NA BSD_3_clause + file LICENSE NANANANAyes4.3.0
zlibbioczlibbioc /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.48.0 NA NA NA NA BiocStyle NA Artistic-2.0 + file LICENSE NANANANAyes4.3.2
zoozoo /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/R/library1.8-12 NA R (>= 3.1.0), stats utils, graphics, grDevices, lattice (>= 0.20-27) NA AER, coda, chron, ggplot2 (>= 3.0.0), mondate, scales, stinepack, strucchange, timeDate, timeSeries, tis, tseries, xts NA GPL-2 | GPL-3 NANANANAyes4.3.0
In [9]:
library(Signac)
In [10]:
sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /projects/ps-renlab2/zhw063/miniconda3/envs/seurat5/lib/libopenblasp-r0.3.24.so;  LAPACK version 3.11.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Los_Angeles
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] Signac_1.12.0      Seurat_5.0.0       SeuratObject_5.0.0 sp_2.1-1          

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3      jsonlite_1.8.7          magrittr_2.0.3         
  [4] spatstat.utils_3.0-4    zlibbioc_1.48.0         vctrs_0.6.4            
  [7] ROCR_1.0-11             Rsamtools_2.18.0        spatstat.explore_3.2-5 
 [10] RCurl_1.98-1.13         base64enc_0.1-3         RcppRoll_0.3.0         
 [13] htmltools_0.5.7         curl_5.1.0              sctransform_0.4.1      
 [16] parallelly_1.36.0       KernSmooth_2.23-22      htmlwidgets_1.6.2      
 [19] ica_1.0-3               plyr_1.8.9              plotly_4.10.3          
 [22] zoo_1.8-12              uuid_1.1-1              igraph_1.5.1           
 [25] mime_0.12               lifecycle_1.0.4         pkgconfig_2.0.3        
 [28] Matrix_1.6-3            R6_2.5.1                fastmap_1.1.1          
 [31] GenomeInfoDbData_1.2.11 fitdistrplus_1.1-11     future_1.33.0          
 [34] shiny_1.7.5.1           digest_0.6.33           colorspace_2.1-0       
 [37] patchwork_1.1.3         S4Vectors_0.40.1        tensor_1.5             
 [40] RSpectra_0.16-1         irlba_2.3.5.1           GenomicRanges_1.54.1   
 [43] progressr_0.14.0        fansi_1.0.5             spatstat.sparse_3.0-3  
 [46] httr_1.4.7              polyclip_1.10-6         abind_1.4-5            
 [49] compiler_4.3.2          remotes_2.4.2.1         BiocParallel_1.36.0    
 [52] fastDummies_1.7.3       MASS_7.3-60             tools_4.3.2            
 [55] lmtest_0.9-40           httpuv_1.6.12           future.apply_1.11.0    
 [58] goftest_1.2-3           glue_1.6.2              nlme_3.1-163           
 [61] promises_1.2.1          grid_4.3.2              pbdZMQ_0.3-10          
 [64] Rtsne_0.16              cluster_2.1.4           reshape2_1.4.4         
 [67] generics_0.1.3          gtable_0.3.4            spatstat.data_3.0-3    
 [70] tidyr_1.3.0             data.table_1.14.8       XVector_0.42.0         
 [73] utf8_1.2.4              BiocGenerics_0.48.1     spatstat.geom_3.2-7    
 [76] RcppAnnoy_0.0.21        ggrepel_0.9.4           RANN_2.6.1             
 [79] pillar_1.9.0            stringr_1.5.1           spam_2.10-0            
 [82] IRdisplay_1.1           RcppHNSW_0.5.0          later_1.3.1            
 [85] splines_4.3.2           dplyr_1.1.3             lattice_0.22-5         
 [88] survival_3.5-7          deldir_1.0-9            tidyselect_1.2.0       
 [91] Biostrings_2.70.1       miniUI_0.1.1.1          pbapply_1.7-2          
 [94] gridExtra_2.3           IRanges_2.36.0          scattermore_1.2        
 [97] stats4_4.3.2            matrixStats_1.1.0       stringi_1.8.1          
[100] lazyeval_0.2.2          evaluate_0.23           codetools_0.2-19       
[103] tibble_3.2.1            cli_3.6.1               uwot_0.1.16            
[106] IRkernel_1.3.2          xtable_1.8-4            reticulate_1.34.0      
[109] repr_1.1.6              munsell_0.5.0           Rcpp_1.0.11            
[112] GenomeInfoDb_1.38.1     globals_0.16.2          spatstat.random_3.2-1  
[115] png_0.1-8               parallel_4.3.2          ellipsis_0.3.2         
[118] ggplot2_3.4.4           dotCall64_1.1-0         bitops_1.0-7           
[121] listenv_0.9.0           viridisLite_0.4.2       scales_1.2.1           
[124] ggridges_0.5.4          leiden_0.4.3            purrr_1.0.2            
[127] crayon_1.5.2            rlang_1.1.2             fastmatch_1.1-4        
[130] cowplot_1.1.1          
In [8]:
install.packages("remotes")
In [5]:
remotes::install_github("bnprks/BPCells")
Skipping install of 'BPCells' from a github remote, the SHA1 (75778b4a) has not changed since last install.
  Use `force = TRUE` to force installation

In [6]:
options(repr.plot.width=16, repr.plot.height=10)
In [7]:
library(ggforce)
library(ggseqlogo)
library(pheatmap)
library(harmony)
library(EnsDb.Mmusculus.v79)
library(tibble)
Error in library(ggforce): there is no package called ‘ggforce’
Traceback:

1. library(ggforce)
In [8]:
setwd('/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/')

Experiment 1¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/01.MouseBrainExp1/merge_mtx

In [5]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/01.MouseBrainExp1/merge_mtx/MouseBrainExp1_RNA/")
In [6]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [7]:
brain
An object of class Seurat 
48517 features across 43814 samples within 1 assay 
Active assay: RNA (48517 features, 0 variable features)
In [8]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    389     919    1403    1647    2128    6630 
In [9]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1400    2406    3281    4202   19995 
In [10]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1750  0.1739 13.1579 
In [11]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
No description has been provided for this image
In [12]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
In [13]:
brain
An object of class Seurat 
48517 features across 43814 samples within 1 assay 
Active assay: RNA (48517 features, 0 variable features)
In [14]:
brain.test
An object of class Seurat 
48517 features across 43122 samples within 1 assay 
Active assay: RNA (48517 features, 0 variable features)
In [15]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [16]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    501     926    1412    1648    2132    5997 
In [17]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1411    2426    3268    4217   17983 
In [18]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1316  0.1650  1.9980 
In [19]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
2129 2185 1181 1631 1937 2286  960 1217 5062 4611 3437 2328 3886 4370 2902 3000 
In [20]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"HCp"

table(brainregion)
brainregion
  CPU   HCa   HCp   HYP 
13987 12479  8079  8577 
In [21]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   13526    29596 
In [22]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp1.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 A01  A02  A03  A04  A05  A06  A07  A08  A09  A10  A11  A12  A13  A14  A15  A16 
2396 1771 1610 2325 2443 2263 2261 1429 1734 1400 1939 1556 1788 1922 1830 1679 
 A17  A18  A19  A20  A21  A22  A23 
1494 1460 1810 2275 2343 1848 1546 
In [26]:
saveRDS(brain.exp1.rna, file = "01.MouseBrainExp1/merge_mtx/brain.exp1.rna.object.rds")

Experiment 2¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/02.MouseBrainExp2/merge_mtx/MouseBrainExp2_RNA

In [29]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/02.MouseBrainExp2/merge_mtx/MouseBrainExp2_RNA")
In [30]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [32]:
brain
An object of class Seurat 
50750 features across 85899 samples within 1 assay 
Active assay: RNA (50750 features, 0 variable features)
In [33]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    202     738     941    1054    1241    5807 
In [34]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    700    1090    1474    1778    2098   16284 
In [35]:
summary(brain@meta.data$percent.mt)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.00000  0.00000  0.00000  0.06421  0.00000 14.94058 
In [36]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
No description has been provided for this image
In [37]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 4000 & percent.mt < 2 & nCount_RNA < 12000)
brain
An object of class Seurat 
50750 features across 85899 samples within 1 assay 
Active assay: RNA (50750 features, 0 variable features)
In [38]:
brain.test
An object of class Seurat 
50750 features across 85133 samples within 1 assay 
Active assay: RNA (50750 features, 0 variable features)
In [39]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [40]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
7219 7452 4252 3661 2999 3977 3976 5574 7363 8150 5298 5209 4865 6130 4435 4573 
In [41]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"HCp"

table(brainregion)
brainregion
  CPU   HCa   HCp   HYP 
30184 17971 18558 18420 
In [42]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
  39110   46023 
In [43]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp2.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 B01  B02  B03  B04  B05  B06  B07  B08  B09  B10  B11  B12  B13  B14  B15  B16 
1664 1521 1908 1285 2049 2013 1546 2016 1471 1698 1908 1848 1818 1405 1740 2235 
 B17  B18  B19  B20  B21  B22  B23  B24  B25  B26  B27  B28  B29  B30  B31  B32 
1653 1492 1628 1543 1738 1667 1631 1958 2306 1979 2056 2125 2032 1969 2209 2387 
 B33  B34  B35  B36  B37  B38  B39  B40  B41  B42  B43  B44  B45 
2136 2066 2359 2198 2167 2359 2127 1701 1667 1748 1760 2361 1986 
In [44]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp2.rna'
In [45]:
saveRDS(brain.exp2.rna, file = "02.MouseBrainExp2/merge_mtx/brain.exp2.rna.object.rds")

Experiment 3¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/03.MouseBrainExp3/merge_mtx/

In [46]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/03.MouseBrainExp3/merge_mtx/MouseBrainExp3_RNA")
In [47]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [49]:
brain
An object of class Seurat 
52159 features across 61457 samples within 1 assay 
Active assay: RNA (52159 features, 0 variable features)
In [50]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      2     898    1453    1731    2309    7764 
In [51]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      3    1322    2355    3537    4686   29993 
In [52]:
summary(brain@meta.data$percent.mt)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  0.0000   0.0000   0.0320   0.4795   0.4797 100.0000 
In [53]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
No description has been provided for this image
In [54]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 
52159 features across 61457 samples within 1 assay 
Active assay: RNA (52159 features, 0 variable features)
In [55]:
brain.test
An object of class Seurat 
52159 features across 56486 samples within 1 assay 
Active assay: RNA (52159 features, 0 variable features)
In [56]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [57]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
1854 1340 1250 1062  509  622 1284 2298  541  386 6715 4590 3651 5468 1221 1281 
  17   18   19   20   21   22   23   24 
2097 3544 2228 3195 6220 3337 1407  386 
In [58]:
table(bc[,1])
 C01  C02  C03  C04  C05  C06  C07  C08  C09  C10  C11  C12  C13  C14  C15  C16 
1316 1456 1128 1177 1209 1214 1197 1355 1220 1203 1256 1085 1055 1041 1019 1051 
 C17  C18  C19  C20  C21  C22  C23  C24  C25  C26  C27  C28  C29  C30  C31  C32 
 989 1037 1017 1109 1016 1055 1195 1090 1059 1112  855  843 1244 1262 1193 1174 
 C33  C34  C35  C36  C37  C38  C39  C40  C41  C42  C43  C44  C45  C46  C47  C48 
1107 1076 1092  994  992 1023  996 1087 1057  975    1 1301 1126  913 1185 1065 
 C49  C50  C51  C52 
1066 1114 1046 1038 
In [59]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="13"|bc[,4]=="14"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="15"|bc[,4]=="16"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="17"|bc[,4]=="18"]<-"VTA_SnR"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="19"|bc[,4]=="20"]<-"PFC"
brainregion[bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"mESC"


table(brainregion)
brainregion
    AMY     ERC    mESC     NAC     PFC VTA_SnR 
  11431   14499   11350    3633    6350    9223 
In [60]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   20703    35783 
In [61]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp3.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 C01  C02  C03  C04  C05  C06  C07  C08  C09  C10  C11  C12  C13  C14  C15  C16 
1316 1456 1128 1177 1209 1214 1197 1355 1220 1203 1256 1085 1055 1041 1019 1051 
 C17  C18  C19  C20  C21  C22  C23  C24  C25  C26  C27  C28  C29  C30  C31  C32 
 989 1037 1017 1109 1016 1055 1195 1090 1059 1112  855  843 1244 1262 1193 1174 
 C33  C34  C35  C36  C37  C38  C39  C40  C41  C42  C43  C44  C45  C46  C47  C48 
1107 1076 1092  994  992 1023  996 1087 1057  975    1 1301 1126  913 1185 1065 
 C49  C50  C51  C52 
1066 1114 1046 1038 
In [62]:
saveRDS(brain.exp3.rna, file = "03.MouseBrainExp3/merge_mtx/brain.exp3.rna.object.rds")

Experiment 4¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/04.MouseBrainExp4/merge_mtx/

In [76]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/04.MouseBrainExp4/merge_mtx/MouseBrainExp4_RNA")
In [77]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [78]:
brain
An object of class Seurat 
51507 features across 65640 samples within 1 assay 
Active assay: RNA (51507 features, 0 variable features)
In [79]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     57     908    1244    1420    1725    7607 
In [80]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    502    1418    2098    2697    3252   29696 
In [81]:
summary(brain@meta.data$percent.mt)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.00000  0.00000  0.00000  0.05371  0.00000 11.18144 
In [82]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
No description has been provided for this image
In [83]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 
51507 features across 65640 samples within 1 assay 
Active assay: RNA (51507 features, 0 variable features)
In [84]:
brain.test
An object of class Seurat 
51507 features across 65065 samples within 1 assay 
Active assay: RNA (51507 features, 0 variable features)
In [85]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [86]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
3211 4351 3058 2881 2422 3142 1908 2211 1058 1275 4502 2440 4952 3820 3137 3130 
  17   18   19   20   21   22   23   24 
3148 2669 2380 2531  463  397 3700 2279 
In [87]:
table(bc[,1])
 D01  D02  D03  D04  D05  D06  D07  D08  D09  D10  D11  D12  D13  D14  D15  D16 
1685 1801 1439 1754 1711 1671 1851 1752 1401 1386 1391 1364 1505 1694 1554 1636 
 D17  D18  D19  D20  D21  D22  D23  D24  D25  D26  D27  D28  D29  D30  D31  D32 
1570 1687 1572 1617 1731 1590 1561 1655 1631 1519 1676 1749 1690 1093 1237 1291 
 D33  D34  D35  D36  D37  D38  D39  D40  D41 
1318 1217 1850 1932 1856 1833 1599 1420 1576 
In [88]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="15"|bc[,4]=="16"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="11"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="23"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="12"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="24"]<-"VTA_SnR"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"

table(brainregion)
brainregion
    AMY     ERC     NAC     PFC VTA_SnR 
  12206   16334   19583    3193   13749 
In [89]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
  32459   32606 
In [90]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp4.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 D01  D02  D03  D04  D05  D06  D07  D08  D09  D10  D11  D12  D13  D14  D15  D16 
1685 1801 1439 1754 1711 1671 1851 1752 1401 1386 1391 1364 1505 1694 1554 1636 
 D17  D18  D19  D20  D21  D22  D23  D24  D25  D26  D27  D28  D29  D30  D31  D32 
1570 1687 1572 1617 1731 1590 1561 1655 1631 1519 1676 1749 1690 1093 1237 1291 
 D33  D34  D35  D36  D37  D38  D39  D40  D41 
1318 1217 1850 1932 1856 1833 1599 1420 1576 
In [92]:
brain.exp4.rna <- brain.exp4.1.rna
rm(brain.exp4.1.rna)

saveRDS(brain.exp4.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/04.MouseBrainExp4/merge_mtx/brain.exp4.rna.object.rds")

Experiment 5¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/05.MouseBrainExp5/merge_mtx/MouseBrainExp5_RNA

In [95]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/05.MouseBrainExp5/merge_mtx/MouseBrainExp5_RNA")
In [96]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [97]:
brain
An object of class Seurat 
52811 features across 164754 samples within 1 assay 
Active assay: RNA (52811 features, 0 variable features)
In [98]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    239     882    1170    1347    1606    7760 
In [99]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1335    1907    2453    2901   27353 
In [100]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1001  0.1224 46.5153 
In [101]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [102]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 
52811 features across 164754 samples within 1 assay 
Active assay: RNA (52811 features, 0 variable features)
In [103]:
brain.test
An object of class Seurat 
52811 features across 164042 samples within 1 assay 
Active assay: RNA (52811 features, 0 variable features)
In [104]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [105]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
 5919  6114  4019  5090  3561  4211  4136  5405  4460  4484  3279  1838  2112 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
 3936  2193  1759  9753 11512  7714  6649  8244  7108  7607  5788  5826  6398 
   27    28    29    30    31    32    33    34 
 4316  3364  5489  6610  2157  2983     5     3 
In [106]:
table(bc[,1])
 E01  E02  E03  E04  E05  E06  E07  E08  E09  E10  E11  E12  E13  E14  E15  E16 
2861 3335 3004 2743 1791 1992 1868 2228 2046 2024 1200 2192 2033 2313 1942 2086 
 E17  E18  E19  E20  E21  E22  E23  E24  E25  E26  E27  E28  E29  E30  E31  E32 
1801 2007 1624 1786 2302 2099 1888 1725 1794 1923 1652 2287 2280 2141 2756 2555 
 E33  E34  E35  E36  E37  E38  E39  E40  E41  E42  E43  E44  E45  E46  E47  E48 
2676 2548 2626 1645 1845 2119 2053 2077 2377 2155 2069 1835 2485 2313 2021 2278 
 E49  E50  E51  E52  E53  E54  E55  E56  E57  E58  E59  E60  E61  E62  E63  E64 
2624 2332 2184 2149 2201 2671 2729 2664 1620  620 2902 2332 2268 1857 2396 2049 
 E65  E66  E67  E68  E69  E70  E71  E72  E73  E74  E75  E76 
2333 2472 2189 1755 1705  894 1643 1460 2914 2799 2698 2182 
In [107]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"
brainregion[bc[,4]=="33"|bc[,4]=="34"]<-"ITGremove"



table(brainregion)
brainregion
      AMY       CPU       ERC       HCa       HCp       HYP ITGremove       NAC 
    12797     33298     21168     23124     22936     23472         8     18147 
  VTA_SnR 
     9092 
In [108]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   62524   101518 
In [109]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp5.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 E01  E02  E03  E04  E05  E06  E07  E08  E09  E10  E11  E12  E13  E14  E15  E16 
2861 3335 3004 2743 1791 1992 1868 2228 2046 2024 1200 2192 2033 2313 1942 2086 
 E17  E18  E19  E20  E21  E22  E23  E24  E25  E26  E27  E28  E29  E30  E31  E32 
1801 2007 1624 1786 2302 2099 1888 1725 1794 1923 1652 2287 2280 2141 2756 2555 
 E33  E34  E35  E36  E37  E38  E39  E40  E41  E42  E43  E44  E45  E46  E47  E48 
2676 2548 2626 1645 1845 2119 2053 2077 2377 2155 2069 1835 2485 2313 2021 2278 
 E49  E50  E51  E52  E53  E54  E55  E56  E57  E58  E59  E60  E61  E62  E63  E64 
2624 2332 2184 2149 2201 2671 2729 2664 1620  620 2902 2332 2268 1857 2396 2049 
 E65  E66  E67  E68  E69  E70  E71  E72  E73  E74  E75  E76 
2333 2472 2189 1755 1705  894 1643 1460 2914 2799 2698 2182 
In [110]:
saveRDS(brain.exp5.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/05.MouseBrainExp5/merge_mtx/brain.exp5.rna.object.rds")
In [111]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp2.rna'
  3. 'brain.exp3.rna'
  4. 'brain.exp4.rna'
  5. 'brain.exp5.rna'

Experiment 6¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/06.MouseBrainExp6/merge_mtx

In [8]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/06.MouseBrainExp6/merge_mtx/MouseBrainExp6_RNA")
In [113]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [114]:
brain
An object of class Seurat 
49918 features across 50684 samples within 1 assay 
Active assay: RNA (49918 features, 0 variable features)
In [115]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    428     711     884    1023    1172    7113 
In [116]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800     998    1294    1618    1843   25241 
In [117]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.0631  0.0000 11.2921 
In [118]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
No description has been provided for this image
In [119]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 15000)
brain
An object of class Seurat 
49918 features across 50684 samples within 1 assay 
Active assay: RNA (49918 features, 0 variable features)
In [120]:
brain.test
An object of class Seurat 
49918 features across 50540 samples within 1 assay 
Active assay: RNA (49918 features, 0 variable features)
In [121]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [122]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
1741 2390  955 1039 1337 1027 1564 2651 2302 2384 1656 1375 3438 3877 1000  840 
  17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
1260 1781 1244 1433 1956 1507 2518 1861  889  999 1009  658 1031 1746  526  546 
In [123]:
table(bc[,1])
 F01  F02  F03  F04  F05  F06  F07  F08  F09  F10  F11  F12  F13  F14  F15  F16 
 706  921  772   69  756  700  642  635  816  825  825  816  496  506  529  518 
 F17  F18  F19  F20  F21  F22  F23  F24  F25  F26  F27  F28  F29  F30  F31  F32 
 714  492  522  894  698  541  504  443 1838 1705 1756 1742 1679 1724 1808 1480 
 F33  F34  F35  F36  F37  F38  F39  F40  F41  F42  F43  F44  F45  F46  F47  F48 
1530 1458 1524 1452  294  299  493  693  423  409  698  209  418  576  606  391 
 F49  F50  F51  F52  F53  F54 
1495  918 1872 1926 1892 1892 
In [124]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"


table(brainregion)
brainregion
    AMY     CPU     ERC     HCa     HCp     HYP     NAC VTA_SnR 
   4698    7172    6574    5827    8594    4671   10092    2912 
In [125]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
  29576   20964 
In [126]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp6.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 F01  F02  F03  F04  F05  F06  F07  F08  F09  F10  F11  F12  F13  F14  F15  F16 
 706  921  772   69  756  700  642  635  816  825  825  816  496  506  529  518 
 F17  F18  F19  F20  F21  F22  F23  F24  F25  F26  F27  F28  F29  F30  F31  F32 
 714  492  522  894  698  541  504  443 1838 1705 1756 1742 1679 1724 1808 1480 
 F33  F34  F35  F36  F37  F38  F39  F40  F41  F42  F43  F44  F45  F46  F47  F48 
1530 1458 1524 1452  294  299  493  693  423  409  698  209  418  576  606  391 
 F49  F50  F51  F52  F53  F54 
1495  918 1872 1926 1892 1892 
In [127]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp2.rna'
  3. 'brain.exp3.rna'
  4. 'brain.exp4.rna'
  5. 'brain.exp5.rna'
  6. 'brain.exp6.rna'
In [128]:
saveRDS(brain.exp6.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/06.MouseBrainExp6/merge_mtx/brain.exp6.rna.object.rds")

Experiment 7¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/07.MouseBrainExp7/merge_mtx

In [129]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/07.MouseBrainExp7/merge_mtx/MouseBrainExp7_RNA")
In [130]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [131]:
brain
An object of class Seurat 
52563 features across 109718 samples within 1 assay 
Active assay: RNA (52563 features, 0 variable features)
In [132]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    304     840    1138    1363    1619    7995 
In [133]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1244    1811    2441    2836   29471 
In [134]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.2052  0.2020 14.0278 
In [135]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [136]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 
52563 features across 109718 samples within 1 assay 
Active assay: RNA (52563 features, 0 variable features)
In [137]:
brain.test
An object of class Seurat 
52563 features across 107313 samples within 1 assay 
Active assay: RNA (52563 features, 0 variable features)
In [138]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [139]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
3499 3819 2409 2601  798 1416 1392 2814 4621 4366 1839 1930 1322 2089 1941 1724 
  17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
8418 9347 5641 5445 1804 1984 4571 3935 5661 6049 4153 4170 1698 1993 1684 2180 
In [140]:
table(bc[,1])
 G01  G02  G03  G04  G05  G06  G07  G08  G09  G10  G11  G12  G13  G14  G15  G16 
1637 1812 1904 1710 1617 1542 1499 1466 1628 1611 1553 1778 1566 1547 1626 1557 
 G17  G18  G19  G20  G21  G22  G23  G24  G25  G26  G27  G28  G29  G30  G31  G32 
1530 1411 1429 1762 1771 1539 1621 1742 1760 1676 1530 1213 1346 1529 1587 1090 
 G33  G34  G35  G36  G37  G38  G39  G40  G41  G42  G43  G44  G45  G46  G47  G48 
1403 1190 1767 1595 1610 1458 1081 1460  636 1704 1303 1144  583 1471 1482 1471 
 G49  G50  G51  G52  G53  G54  G55  G56  G57  G58  G59  G60  G61  G62  G63  G64 
1394 1323 1094 1312  911 1404 1448 1449 1440 1382 1482 1456 1321  853 1369 1263 
 G65  G66  G67  G68  G69  G70  G71  G72  G73  G74  G75 
1604 1437  715 1438  763 1011  347 1910 1886 1702 1652 
In [141]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"


table(brainregion)
brainregion
    AMY     CPU     ERC     HCa     HCp     HYP     NAC VTA_SnR 
  12092   25083   20697    6002   12712   16096    7102    7529 
In [142]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   38580    68733 
In [143]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp7.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 G01  G02  G03  G04  G05  G06  G07  G08  G09  G10  G11  G12  G13  G14  G15  G16 
1637 1812 1904 1710 1617 1542 1499 1466 1628 1611 1553 1778 1566 1547 1626 1557 
 G17  G18  G19  G20  G21  G22  G23  G24  G25  G26  G27  G28  G29  G30  G31  G32 
1530 1411 1429 1762 1771 1539 1621 1742 1760 1676 1530 1213 1346 1529 1587 1090 
 G33  G34  G35  G36  G37  G38  G39  G40  G41  G42  G43  G44  G45  G46  G47  G48 
1403 1190 1767 1595 1610 1458 1081 1460  636 1704 1303 1144  583 1471 1482 1471 
 G49  G50  G51  G52  G53  G54  G55  G56  G57  G58  G59  G60  G61  G62  G63  G64 
1394 1323 1094 1312  911 1404 1448 1449 1440 1382 1482 1456 1321  853 1369 1263 
 G65  G66  G67  G68  G69  G70  G71  G72  G73  G74  G75 
1604 1437  715 1438  763 1011  347 1910 1886 1702 1652 
In [144]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp2.rna'
  3. 'brain.exp3.rna'
  4. 'brain.exp4.rna'
  5. 'brain.exp5.rna'
  6. 'brain.exp6.rna'
  7. 'brain.exp7.rna'
In [145]:
saveRDS(brain.exp7.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/07.MouseBrainExp7/merge_mtx/brain.exp7.rna.object.rds")

Experiment 8¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/08.MouseBrainExp8/merge_mtx

In [146]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/08.MouseBrainExp8/merge_mtx/MouseBrainExp8_RNA")
In [147]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [148]:
brain
An object of class Seurat 
52547 features across 101018 samples within 1 assay 
Active assay: RNA (52547 features, 0 variable features)
In [149]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    261    1204    1644    1825    2210    7908 
In [150]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1000    1953    2951    3658    4417   29397 
In [151]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1243  0.1100  7.1623 
In [152]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [153]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 
52547 features across 101018 samples within 1 assay 
Active assay: RNA (52547 features, 0 variable features)
In [154]:
brain.test
An object of class Seurat 
52547 features across 99758 samples within 1 assay 
Active assay: RNA (52547 features, 0 variable features)
In [155]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [157]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
 5296  5173  3771  3279  2719  4149  2756  3261 11353 10388  7572  6346  8789 
   14    15    16 
10933  7089  6884 
In [158]:
table(bc[,1])
 H01  H02  H03  H04  H05  H06  H07  H08  H09  H10  H11  H12  H13  H14  H15  H16 
1559 1587 1561 1154 1593 1456 1720 1509 1511 1590 1524 1348 1504 1568 1693 1434 
 H17  H18  H19  H20  H21  H22  H23  H24  H25  H26  H27  H28  H29  H30  H31  H32 
1515 1514 1532 1627 1634 1385 1517 1667 1727 1787 1824 1837 1705 1589 1529 1752 
 H33  H34  H35  H36  H37  H38  H39  H40  H41  H42  H43  H44  H45  H46  H47  H48 
1694 2714 1827 1665 1566 1450 1679 1698 1580 1560 1564 1608 1471 1522 1635 1660 
 H49  H50  H51  H52  H53  H54  H55  H56  H57  H58  H59  H60  H61  H62 
1473 1724 1521 1371 1540 1569 1800 1692 1467 1781 1472 1433 1507 2063 
In [159]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"HCp"

table(brainregion)
brainregion
  CPU   HCa   HCp   HYP 
32210 26590 19990 20968 
In [160]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   30404    69354 
In [161]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp8.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 H01  H02  H03  H04  H05  H06  H07  H08  H09  H10  H11  H12  H13  H14  H15  H16 
1559 1587 1561 1154 1593 1456 1720 1509 1511 1590 1524 1348 1504 1568 1693 1434 
 H17  H18  H19  H20  H21  H22  H23  H24  H25  H26  H27  H28  H29  H30  H31  H32 
1515 1514 1532 1627 1634 1385 1517 1667 1727 1787 1824 1837 1705 1589 1529 1752 
 H33  H34  H35  H36  H37  H38  H39  H40  H41  H42  H43  H44  H45  H46  H47  H48 
1694 2714 1827 1665 1566 1450 1679 1698 1580 1560 1564 1608 1471 1522 1635 1660 
 H49  H50  H51  H52  H53  H54  H55  H56  H57  H58  H59  H60  H61  H62 
1473 1724 1521 1371 1540 1569 1800 1692 1467 1781 1472 1433 1507 2063 
In [162]:
saveRDS(brain.exp8.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/08.MouseBrainExp8/merge_mtx/brain.exp8.rna.object.rds")
In [163]:
VlnPlot(brain.exp8.rna, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0)
No description has been provided for this image

Experiment 9¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/09.MouseBrainExp9/merge_mtx

In [164]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/09.MouseBrainExp9/merge_mtx/MouseBrainExp9_RNA")
In [165]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [166]:
brain
An object of class Seurat 
53144 features across 110223 samples within 1 assay 
Active assay: RNA (53144 features, 0 variable features)
In [167]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    336    1071    1519    1753    2133    7806 
In [168]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1672    2626    3505    4169   29949 
In [169]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.1638  0.3811  0.4986 28.8601 
In [170]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [173]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 7000 & percent.mt < 2 & nCount_RNA < 25000)
brain
An object of class Seurat 
53144 features across 110223 samples within 1 assay 
Active assay: RNA (53144 features, 0 variable features)
In [174]:
brain.test
An object of class Seurat 
53144 features across 102154 samples within 1 assay 
Active assay: RNA (53144 features, 0 variable features)
In [175]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [176]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
8658 8811 6312 6915 4942 5914 3646 3924 6404 9062 7125 7275 7733 7687 4114 3632 
In [177]:
table(bc[,1])
 I01  I02  I03  I04  I05  I06  I07  I08  I09  I10  I11  I12  I13  I14  I15  I16 
1510 1548 1468 1520 1457 1402  945 1230 1219 1228 1196 1220 1292 1440 1376 1285 
 I17  I18  I19  I20  I21  I22  I23  I24  I25  I26  I27  I28  I29  I30  I31  I32 
1252 1261 1167 1184 1093  941 1089 1226 1112  776  915 1193 1197 1262 1177 1240 
 I33  I34  I35  I36  I37  I38  I39  I40  I41  I42  I43  I44  I45  I46  I47  I48 
1043 1157 1175 1162 1267 1277 1306 1326 1376 1290 1077 1327 1420  834  721 1101 
 I49  I50  I51  I52  I53  I54  I55  I56  I57  I58  I59  I60  I61  I62  I63  I64 
1343 1214 1219 1235 1032 1023 1279 1047 1139 1040  901  956 1246  881 1180 1212 
 I65  I66  I67  I68  I69  I70  I71  I72  I73  I74  I75  I76  I77  I78  I79  I80 
1235 1235 1258 1225 1139 1283 1447 1263 1244 1167 1164    5 1229 1182    3 1365 
 I81  I82  I83  I84  I85  I86  I87 
1302 1233 1370 1241 1174 1062 1101 
In [178]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="13"|bc[,4]=="14"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="15"|bc[,4]=="16"]<-"VTA_SnR"

table(brainregion)
brainregion
    AMY     ERC     NAC VTA_SnR 
  27627   32935   26276   15316 
In [179]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
  49122   53032 
In [180]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp9.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 I01  I02  I03  I04  I05  I06  I07  I08  I09  I10  I11  I12  I13  I14  I15  I16 
1510 1548 1468 1520 1457 1402  945 1230 1219 1228 1196 1220 1292 1440 1376 1285 
 I17  I18  I19  I20  I21  I22  I23  I24  I25  I26  I27  I28  I29  I30  I31  I32 
1252 1261 1167 1184 1093  941 1089 1226 1112  776  915 1193 1197 1262 1177 1240 
 I33  I34  I35  I36  I37  I38  I39  I40  I41  I42  I43  I44  I45  I46  I47  I48 
1043 1157 1175 1162 1267 1277 1306 1326 1376 1290 1077 1327 1420  834  721 1101 
 I49  I50  I51  I52  I53  I54  I55  I56  I57  I58  I59  I60  I61  I62  I63  I64 
1343 1214 1219 1235 1032 1023 1279 1047 1139 1040  901  956 1246  881 1180 1212 
 I65  I66  I67  I68  I69  I70  I71  I72  I73  I74  I75  I76  I77  I78  I79  I80 
1235 1235 1258 1225 1139 1283 1447 1263 1244 1167 1164    5 1229 1182    3 1365 
 I81  I82  I83  I84  I85  I86  I87 
1302 1233 1370 1241 1174 1062 1101 
In [181]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp2.rna'
  3. 'brain.exp3.rna'
  4. 'brain.exp4.rna'
  5. 'brain.exp5.rna'
  6. 'brain.exp6.rna'
  7. 'brain.exp7.rna'
  8. 'brain.exp8.rna'
  9. 'brain.exp9.rna'
In [182]:
saveRDS(brain.exp9.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/09.MouseBrainExp9/merge_mtx/brain.exp9.rna.object.rds")

Experiment 10¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/10.MouseBrainExp10/merge_mtx

In [33]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/10.MouseBrainExp10/merge_mtx/MouseBrainExp10_RNA")
In [34]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [35]:
brain
An object of class Seurat 
54029 features across 290298 samples within 1 assay 
Active assay: RNA (54029 features, 0 variable features)
In [36]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    249     965    1364    1548    1894    8044 
In [37]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1489    2319    2963    3609   29860 
In [38]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1150  0.1017 11.7647 
In [39]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [40]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 65000 & percent.mt < 2 & nCount_RNA < 22000)
brain
An object of class Seurat 
54029 features across 290298 samples within 1 assay 
Active assay: RNA (54029 features, 0 variable features)
In [41]:
brain.test
An object of class Seurat 
54029 features across 286685 samples within 1 assay 
Active assay: RNA (54029 features, 0 variable features)
In [42]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [43]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
11843 12803 10924 11134  7147  9475  7368  9215  5108  3321  9877 10057 10846 
   14    15    16    17    18    19    20    21    22    23    24    25    26 
11878  7971  7693 12509 12908 11997  9113  9857  9858  8897  7112  4719  4261 
   27    28    29    30    31    32 
 8395  9543  9200 11851  4357  5448 
In [44]:
table(bc[,1])
 J01  J02  J03  J04  J05  J06  J07  J08  J09  J10  J11  J12  J13  J14  J15  J16 
2119 2219 2103 1991 2099 2132 1915 2003 1937 1956 1971 2034 2159 2192 2082 1939 
 J17  J18  J19  J20  J21  J22  J23  J24  J25  J26  J27  J28  J29  J30  J31  J32 
2138 1212 1929 1932 2030 2017 1585 2216 2268 2127 2257 2036 2120 2809 2266 2014 
 J33  J34  J35  J36  J37  J38  J39  J40  J41  J42  J43  J44  J45  J46  J47  J48 
2302 2060 2330 2015 2005 2134 2160 2002 2102 2271 2103 2473 2310 2392 2145 1995 
 J49  J50  J51  J52  J53  J54  J55  J56  J57  J58  J59  J60  J61  J62  J63  J64 
2171 2432 2441 2331 2349 2169 2185 1996 1849 2030 2127 2105 2221 2393 1725 1822 
 J65  J66  J67  J68  J69  J70  J71  J72  J73  J74  J75  J76  J77  J78  J79  J80 
2213 2442 1703 2107 2300 1929 2111 1976 1892 2041 2207 2094 2189 2030 1908 2133 
 J81  J82  J83  J84  J85  J86  J87  J88  J89  J90  J91  J92  J93  J94  J95  J96 
2251 2430 2117 2100 2083 2142 1829 1734 1637 2026 1830 1812 1890 1838 1420 1455 
 J97  J98  J99  O01  O02  O03  O04  O05  O06  O07  O08  O09  O10  O11  O12  O13 
1503 1965 1971 2002 2117 2273 2178 1935 2014 1929 2084 2213 2132 2271 2183 2268 
 O14  O15  O16  O17  O18  O19  O20  O21  O22  O23  O24  O25  O26  O27  O28  O29 
2126 1967 2058 2098 2065 1864 1945 1927 2148 2162 2127 2033 2060 2076 2128 2037 
 O30  O31  O32  O33  O34  O35  O36  O37  O38  O39  O40 
2024 2016 2035 2011 1717 1859 2035 2183 1966 2093 2066 
In [45]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="19"|bc[,4]=="20"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="21"|bc[,4]=="22"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="23"|bc[,4]=="24"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="25"|bc[,4]=="26"]<-"ERC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="27"|bc[,4]=="28"]<-"AMY"
brainregion[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="29"|bc[,4]=="30"]<-"NAC"
brainregion[bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="31"|bc[,4]=="32"]<-"VTA_SnR"

table(brainregion)
brainregion
    AMY     CPU     ERC     HCa     HCp     HYP     NAC VTA_SnR 
  37872   50063   17409   36337   32592   43168   43775   25469 
In [46]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"|bc[,4]=="25"|bc[,4]=="26"|bc[,4]=="27"|bc[,4]=="28"|bc[,4]=="29"|bc[,4]=="30"|bc[,4]=="31"|bc[,4]=="32"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
 146660  140025 
In [47]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp10.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 J01  J02  J03  J04  J05  J06  J07  J08  J09  J10  J11  J12  J13  J14  J15  J16 
2119 2219 2103 1991 2099 2132 1915 2003 1937 1956 1971 2034 2159 2192 2082 1939 
 J17  J18  J19  J20  J21  J22  J23  J24  J25  J26  J27  J28  J29  J30  J31  J32 
2138 1212 1929 1932 2030 2017 1585 2216 2268 2127 2257 2036 2120 2809 2266 2014 
 J33  J34  J35  J36  J37  J38  J39  J40  J41  J42  J43  J44  J45  J46  J47  J48 
2302 2060 2330 2015 2005 2134 2160 2002 2102 2271 2103 2473 2310 2392 2145 1995 
 J49  J50  J51  J52  J53  J54  J55  J56  J57  J58  J59  J60  J61  J62  J63  J64 
2171 2432 2441 2331 2349 2169 2185 1996 1849 2030 2127 2105 2221 2393 1725 1822 
 J65  J66  J67  J68  J69  J70  J71  J72  J73  J74  J75  J76  J77  J78  J79  J80 
2213 2442 1703 2107 2300 1929 2111 1976 1892 2041 2207 2094 2189 2030 1908 2133 
 J81  J82  J83  J84  J85  J86  J87  J88  J89  J90  J91  J92  J93  J94  J95  J96 
2251 2430 2117 2100 2083 2142 1829 1734 1637 2026 1830 1812 1890 1838 1420 1455 
 J97  J98  J99  O01  O02  O03  O04  O05  O06  O07  O08  O09  O10  O11  O12  O13 
1503 1965 1971 2002 2117 2273 2178 1935 2014 1929 2084 2213 2132 2271 2183 2268 
 O14  O15  O16  O17  O18  O19  O20  O21  O22  O23  O24  O25  O26  O27  O28  O29 
2126 1967 2058 2098 2065 1864 1945 1927 2148 2162 2127 2033 2060 2076 2128 2037 
 O30  O31  O32  O33  O34  O35  O36  O37  O38  O39  O40 
2024 2016 2035 2011 1717 1859 2035 2183 1966 2093 2066 
In [48]:
ls()
  1. 'brain.exp10.rna'
  2. 'brain.exp11.rna'
In [49]:
saveRDS(brain.exp10.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/10.MouseBrainExp10/merge_mtx/brain.exp10.rna.object.rds")

Experiment 11¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/11.MouseBrainExp11/merge_mtx

In [50]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/11.MouseBrainExp11/merge_mtx/MouseBrainExp11_RNA")
In [51]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [52]:
brain
An object of class Seurat 
52671 features across 134099 samples within 1 assay 
Active assay: RNA (52671 features, 0 variable features)
In [53]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    211     790    1046    1255    1468    7805 
In [54]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1189    1701    2301    2640   29966 
In [55]:
summary(brain@meta.data$percent.mt)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.00000  0.00000  0.00000  0.07582  0.00000 20.78544 
In [56]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [57]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 
52671 features across 134099 samples within 1 assay 
Active assay: RNA (52671 features, 0 variable features)
In [58]:
brain.test
An object of class Seurat 
52671 features across 132932 samples within 1 assay 
Active assay: RNA (52671 features, 0 variable features)
In [59]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [60]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
 9234 10475  4183  5134  1404  1520  2302  4458  3304  2828   900  2522 11854 
   14    15    16    17    18    19    20    21    22    23    24 
12359 10047 10778  3367  3949  3416  2740  9406  7709  4438  4605 
In [61]:
table(bc[,1])
 K01  K02  K03  K04  K05  K06  K07  K08  K09  K10  K11  K12  K13  K14  K15  K16 
2748 2432 2411 2426 2302 2149 1646 2036 1883 1953 1290 2088 1916 1862 1645 1627 
 K17  K18  K19  K20  K21  K22  K23  K24  K25  K26  K27  K28  K29  K30  K31  K32 
1072 2195 2245 2197 1800 2059 1613 2182 2080 2086 2028 2273 1944 2176 2047 2216 
 K33  K34  K35  K36  K37  K38  K39  K40  K41  K42  K43  K44  K45  K46  K47  K48 
2259 2121 2105 2332 2179 1958 2185 2003 1729 2155 2292 2154 2122 2236 2082 2174 
 K49  K50  K51  K52  K53  K54  K55  K56  K57  K58  K59  K60  K61  K62  K63  K64 
2006 2152 1957 1739 1897 2158 2002 1714 1901 1802 1857 2200 2106 2112 2113 2165 
 K65 
2338 
In [62]:
brainregion <- rep("ERC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="15"|bc[,4]=="16"]<-"AMY"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="17"|bc[,4]=="18"]<-"NAC"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="19"|bc[,4]=="20"]<-"VTA_SnR"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"
brainregion[bc[,4]=="11"|bc[,4]=="23"]<-"NAC"
brainregion[bc[,4]=="12"|bc[,4]=="24"]<-"PFC"


table(brainregion)
brainregion
    AMY     ERC     NAC     PFC VTA_SnR 
  30142   43922   15578   30374   12916 
In [63]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="26"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   59042    73890 
In [64]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp11.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 K01  K02  K03  K04  K05  K06  K07  K08  K09  K10  K11  K12  K13  K14  K15  K16 
2748 2432 2411 2426 2302 2149 1646 2036 1883 1953 1290 2088 1916 1862 1645 1627 
 K17  K18  K19  K20  K21  K22  K23  K24  K25  K26  K27  K28  K29  K30  K31  K32 
1072 2195 2245 2197 1800 2059 1613 2182 2080 2086 2028 2273 1944 2176 2047 2216 
 K33  K34  K35  K36  K37  K38  K39  K40  K41  K42  K43  K44  K45  K46  K47  K48 
2259 2121 2105 2332 2179 1958 2185 2003 1729 2155 2292 2154 2122 2236 2082 2174 
 K49  K50  K51  K52  K53  K54  K55  K56  K57  K58  K59  K60  K61  K62  K63  K64 
2006 2152 1957 1739 1897 2158 2002 1714 1901 1802 1857 2200 2106 2112 2113 2165 
 K65 
2338 
In [65]:
ls()
  1. 'brain.exp10.rna'
  2. 'brain.exp11.rna'
In [66]:
saveRDS(brain.exp11.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/11.MouseBrainExp11/merge_mtx/brain.exp11.rna.object.rds")

Experiment 12¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/12.MouseBrainExp12/merge_mtx

In [68]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/12.MouseBrainExp12/merge_mtx/MouseBrainExp12_RNA")
In [69]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [70]:
brain
An object of class Seurat 
52455 features across 134226 samples within 1 assay 
Active assay: RNA (52455 features, 0 variable features)
In [71]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    233     818    1063    1235    1426    8390 
In [72]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1214    1701    2165    2490   29996 
In [73]:
summary(brain@meta.data$percent.mt)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.00000  0.00000  0.00000  0.08914  0.07619 11.66566 
In [74]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [75]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 18000)
brain
An object of class Seurat 
52455 features across 134226 samples within 1 assay 
Active assay: RNA (52455 features, 0 variable features)
In [76]:
brain.test
An object of class Seurat 
52455 features across 133374 samples within 1 assay 
Active assay: RNA (52455 features, 0 variable features)
In [77]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [78]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
10772 11579  5920  6289  3527  4311  7529 10396  1390   694   813   789 12156 
   14    15    16    17    18    19    20    21    22    23    24 
12578  8027  7648  3656  5274  9682  7994   789   770   367   424 
In [79]:
table(bc[,1])
 L01  L02  L03  L04  L05  L06  L07  L08  L09  L10  L11  L12  L13  L14  L15  L16 
2799 2794 2486 2481 2515 2505 2761 2964 2338 2587 2090 2449 2017 2811 2537 2603 
 L17  L18  L19  L20  L21  L22  L23  L24  L25  L26  L27  L28  L29  L30  L31  L32 
2515 2655 2030 2552 2008 1264 2519 2806 1134 2546 1867 1997 1659 2353 2429 2866 
 L33  L34  L35  L36  L37  L38  L39  L40  L41  L42  L43  L44  L45  L46  L47  L48 
2404 2740 2643 2259 2800 2814 2089 2850 2812 2644 2851 2458 2126 2568 2635 1968 
 L49  L50  L51  L52  L53  L54 
2358 2897 2883 2985 2944 2709 
In [80]:
brainregion <- rep("CPU",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="15"|bc[,4]=="16"]<-"HYP"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="17"|bc[,4]=="18"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"|bc[,4]=="19"|bc[,4]=="20"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="23"|bc[,4]=="24"]<-"PFC"

table(brainregion)
brainregion
  CPU   HCa   HCp   HYP   PFC 
47085 16768 35601 27884  6036 
In [81]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="26"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
  71657   61717 
In [82]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp12.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 L01  L02  L03  L04  L05  L06  L07  L08  L09  L10  L11  L12  L13  L14  L15  L16 
2799 2794 2486 2481 2515 2505 2761 2964 2338 2587 2090 2449 2017 2811 2537 2603 
 L17  L18  L19  L20  L21  L22  L23  L24  L25  L26  L27  L28  L29  L30  L31  L32 
2515 2655 2030 2552 2008 1264 2519 2806 1134 2546 1867 1997 1659 2353 2429 2866 
 L33  L34  L35  L36  L37  L38  L39  L40  L41  L42  L43  L44  L45  L46  L47  L48 
2404 2740 2643 2259 2800 2814 2089 2850 2812 2644 2851 2458 2126 2568 2635 1968 
 L49  L50  L51  L52  L53  L54 
2358 2897 2883 2985 2944 2709 
In [83]:
ls()
  1. 'brain.exp10.rna'
  2. 'brain.exp11.rna'
  3. 'brain.exp12.rna'
In [84]:
saveRDS(brain.exp12.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/12.MouseBrainExp12/merge_mtx/brain.exp12.rna.object.rds")

Experiment 13¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/13.MouseBrainExp13/merge_mtx

In [85]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/13.MouseBrainExp13/merge_mtx/MouseBrainExp13_RNA")
In [86]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [87]:
brain
An object of class Seurat 
52512 features across 120342 samples within 1 assay 
Active assay: RNA (52512 features, 0 variable features)
In [88]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    276     800    1054    1234    1439    7670 
In [89]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    800    1170    1668    2188    2527   29403 
In [90]:
summary(brain@meta.data$percent.mt)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
 0.00000  0.00000  0.00000  0.09725  0.08549 12.24276 
In [91]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

No description has been provided for this image
In [92]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 
52512 features across 120342 samples within 1 assay 
Active assay: RNA (52512 features, 0 variable features)
In [93]:
brain.test
An object of class Seurat 
52512 features across 119644 samples within 1 assay 
Active assay: RNA (52512 features, 0 variable features)
In [94]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [95]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
 1860  1511  2472  2717  4810  6075  6227  8602  4377  5036  1430  1708  6930 
   14    15    16    17    18    19    20    21    22    23 
12814  8189  5452  9087 12146  4454  3429  4516  5801     1 
In [96]:
table(bc[,1])
 M01  M02  M03  M04  M05  M06  M07  M08  M09  M10  M11  M12  M13  M14  M15  M16 
2376 2555 2641 2752 2320 2121 2725 2537 2490 2302 2572 2583 2447 2889 2667 2567 
 M17  M18  M19  M20  M21  M22  M23  M24  M25  M26  M27  M28  M29  M30  M31  M32 
2579 2510 2718 2713 2032 2703 2695 2401 2489 2406 2931 1750 2324 2790 2535 1989 
 M33  M34  M35  M36  M37  M38  M39  M40  M41  M42  M43  M44  M45  M46  M47  M48 
2276 2271 2606 1997 2348 2526 1667 2199 2313 1615 1823 1067 2425 2187 1271  600 
 M49  M50  M51  M52  M53  M54 
1923  866 1493 1418 1886  758 
In [97]:
brainregion <- rep("PFC",dim(bc)[1])
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="17"|bc[,4]=="18"]<-"HCa"
brainregion[bc[,4]=="07"|bc[,4]=="08"]<-"HCp"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="19"|bc[,4]=="20"]<-"VTA_SnR"
brainregion[bc[,4]=="11"|bc[,4]=="12"|bc[,4]=="21"|bc[,4]=="22"]<-"PFC"
brainregion[bc[,4]=="23"]<-"remove"

table(brainregion)
brainregion
    HCa     HCp     PFC  remove VTA_SnR 
  32118   14829   55400       1   17296 
In [98]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   52278    67366 
In [99]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp13.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 M01  M02  M03  M04  M05  M06  M07  M08  M09  M10  M11  M12  M13  M14  M15  M16 
2376 2555 2641 2752 2320 2121 2725 2537 2490 2302 2572 2583 2447 2889 2667 2567 
 M17  M18  M19  M20  M21  M22  M23  M24  M25  M26  M27  M28  M29  M30  M31  M32 
2579 2510 2718 2713 2032 2703 2695 2401 2489 2406 2931 1750 2324 2790 2535 1989 
 M33  M34  M35  M36  M37  M38  M39  M40  M41  M42  M43  M44  M45  M46  M47  M48 
2276 2271 2606 1997 2348 2526 1667 2199 2313 1615 1823 1067 2425 2187 1271  600 
 M49  M50  M51  M52  M53  M54 
1923  866 1493 1418 1886  758 
In [7]:
ls()
In [101]:
saveRDS(brain.exp13.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/13.MouseBrainExp13/merge_mtx/brain.exp13.rna.object.rds")

Experiment 14¶

/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/14.MouseBrainExp14/merge_mtx

In [7]:
brain.data <- Read10X(data.dir = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/14.MouseBrainExp14/merge_mtx/MouseBrainExp14_RNA_new")
In [8]:
brain <- CreateSeuratObject(counts = brain.data, project = "mousebrain", min.cells = 0, min.features = 0)
brain[["percent.mt"]]<-PercentageFeatureSet(brain, pattern="^mt-")
options(repr.plot.width=16, repr.plot.height=10)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [9]:
brain
An object of class Seurat 
52888 features across 97694 samples within 1 assay 
Active assay: RNA (52888 features, 0 variable features)
In [10]:
summary(brain@meta.data$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    326     781    1049    1250    1488    8325 
In [11]:
summary(brain@meta.data$nCount_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    500    1128    1634    2199    2585   29738 
In [12]:
summary(brain@meta.data$percent.mt)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1522  0.1938 12.3211 
In [13]:
plot1 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(brain, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
No description has been provided for this image
In [14]:
brain.test <- subset(brain, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 2 & nCount_RNA < 20000)
brain
An object of class Seurat 
52888 features across 97694 samples within 1 assay 
Active assay: RNA (52888 features, 0 variable features)
In [15]:
brain.test
An object of class Seurat 
52888 features across 96677 samples within 1 assay 
Active assay: RNA (52888 features, 0 variable features)
In [17]:
brain<-brain.test;rm(brain.test)
VlnPlot(brain, features=c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol=3, pt.size=0.5)
No description has been provided for this image
In [18]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
1286 1726 4797 3008 2443 3354 2532 3720 3697 3785 5640 4732 4340 3130 3979 3305 
  17   18   19   20   21   22   23   24 
5283 5916 3486 2758 6647 6528 4686 5899 
In [19]:
table(bc[,1])
 N01  N02  N03  N04  N05  N06  N07  N08  N09  N10  N11  N12  N13  N14  N15  N16 
1735 1682 1493 1334 1566 1870 1693 1544 1620 1608 1418 1431 1591 1593 1483 1460 
 N17  N18  N19  N20  N21  N22  N23  N24  N25  N26  N27  N28  N29  N30  N31  N32 
1340 1470 1612 1497 1691 1543 1683 1693 1749 1149 1097 1001 1204 1215 1262 1343 
 N33  N34  N35  N36  N37  N38  N39  N40  N41  N42  N43  N44  N45  N46  N47  N48 
1203 1144 1413 1480 1347 1475 1614 1557 1428 1244 1065 1263 1710 1672 1612 1041 
 N49  N50  N51  N52  N53  N54  N55  N56  N57  N58  N59  N60  N61  N62 
1648 1774 1879 1917 2110 1563 1652 1936 1695 2102 2126 2207 2039 2091 
In [20]:
brainregion <- rep("PFC",dim(bc)[1])
brainregion[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="11"|bc[,4]=="12"]<-"HCa"
brainregion[bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="15"|bc[,4]=="16"]<-"ERC"
brainregion[bc[,4]=="07"|bc[,4]=="08"]<-"HYP"
brainregion[bc[,4]=="09"|bc[,4]=="10"|bc[,4]=="19"|bc[,4]=="20"]<-"VTA_SnR"
brainregion[bc[,4]=="17"|bc[,4]=="18"]<-"AMY"
brainregion[bc[,4]=="21"|bc[,4]=="22"]<-"HCa"

table(brainregion)
brainregion
    AMY     ERC     HCa     HYP     PFC VTA_SnR 
  11199   13081   31352    6252   21067   13726 
In [21]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
  44025   52652 
In [22]:
brain@meta.data<-cbind(brain@meta.data, cbind(brainregion, modality))
oriBarcode<-bc[,4]
table(bc[,1])
sublib <- bc[,1]
brain@meta.data<-cbind(brain@meta.data, cbind(oriBarcode,sublib))
brain.exp14.rna <- brain

rm(brain.data,brain,bc,plot1,plot2,brainregion,modality,oriBarcode,sublib)
 N01  N02  N03  N04  N05  N06  N07  N08  N09  N10  N11  N12  N13  N14  N15  N16 
1735 1682 1493 1334 1566 1870 1693 1544 1620 1608 1418 1431 1591 1593 1483 1460 
 N17  N18  N19  N20  N21  N22  N23  N24  N25  N26  N27  N28  N29  N30  N31  N32 
1340 1470 1612 1497 1691 1543 1683 1693 1749 1149 1097 1001 1204 1215 1262 1343 
 N33  N34  N35  N36  N37  N38  N39  N40  N41  N42  N43  N44  N45  N46  N47  N48 
1203 1144 1413 1480 1347 1475 1614 1557 1428 1244 1065 1263 1710 1672 1612 1041 
 N49  N50  N51  N52  N53  N54  N55  N56  N57  N58  N59  N60  N61  N62 
1648 1774 1879 1917 2110 1563 1652 1936 1695 2102 2126 2207 2039 2091 
In [23]:
ls()
'brain.exp14.rna'
In [24]:
saveRDS(brain.exp14.rna, file = "/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag/14.MouseBrainExp14/merge_mtx/brain.exp14.rna.object.rds")

Merge all RNA objects¶

In [11]:
library(sctransform)
In [8]:
sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /projects/ps-renlab2/zhw063/miniconda3/envs/singlecell2/lib/libopenblasp-r0.3.21.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] tibble_3.2.1               EnsDb.Mmusculus.v79_2.99.0
 [3] ensembldb_2.22.0           AnnotationFilter_1.22.0   
 [5] GenomicFeatures_1.50.2     AnnotationDbi_1.60.0      
 [7] Biobase_2.58.0             GenomicRanges_1.50.0      
 [9] GenomeInfoDb_1.34.8        IRanges_2.32.0            
[11] harmony_1.0                Rcpp_1.0.10               
[13] pheatmap_1.0.12            ggseqlogo_0.1             
[15] ggforce_0.4.1              S4Vectors_0.36.0          
[17] BiocGenerics_0.44.0        RColorBrewer_1.1-3        
[19] dplyr_1.1.1                cowplot_1.1.1             
[21] ggridges_0.5.4             ggrepel_0.9.3             
[23] ggplot2_3.4.2              Signac_1.9.0              
[25] SeuratObject_4.1.3         Seurat_4.3.0              
[27] sctransform_0.3.5         

loaded via a namespace (and not attached):
  [1] utf8_1.2.3                  spatstat.explore_3.1-0     
  [3] reticulate_1.25             tidyselect_1.2.0           
  [5] RSQLite_2.3.1               htmlwidgets_1.6.2          
  [7] grid_4.2.3                  BiocParallel_1.32.5        
  [9] Rtsne_0.16                  munsell_0.5.0              
 [11] codetools_0.2-19            ica_1.0-3                  
 [13] pbdZMQ_0.3-9                future_1.32.0              
 [15] miniUI_0.1.1.1              withr_2.5.0                
 [17] spatstat.random_3.1-4       colorspace_2.1-0           
 [19] progressr_0.13.0            filelock_1.0.2             
 [21] uuid_1.1-0                  ROCR_1.0-11                
 [23] tensor_1.5                  listenv_0.9.0              
 [25] MatrixGenerics_1.10.0       repr_1.1.6                 
 [27] GenomeInfoDbData_1.2.9      polyclip_1.10-4            
 [29] bit64_4.0.5                 farver_2.1.1               
 [31] parallelly_1.35.0           vctrs_0.6.1                
 [33] generics_0.1.3              BiocFileCache_2.6.0        
 [35] R6_2.5.1                    bitops_1.0-7               
 [37] spatstat.utils_3.0-2        cachem_1.0.7               
 [39] DelayedArray_0.24.0         promises_1.2.0.1           
 [41] BiocIO_1.8.0                scales_1.2.1               
 [43] gtable_0.3.3                globals_0.16.2             
 [45] goftest_1.2-3               rlang_1.1.0                
 [47] RcppRoll_0.3.0              splines_4.2.3              
 [49] rtracklayer_1.58.0          lazyeval_0.2.2             
 [51] spatstat.geom_3.1-0         yaml_2.3.7                 
 [53] reshape2_1.4.4              abind_1.4-5                
 [55] httpuv_1.6.9                tools_4.2.3                
 [57] ellipsis_0.3.2              plyr_1.8.8                 
 [59] base64enc_0.1-3             progress_1.2.2             
 [61] zlibbioc_1.44.0             purrr_1.0.1                
 [63] RCurl_1.98-1.12             prettyunits_1.1.1          
 [65] deldir_1.0-6                pbapply_1.7-0              
 [67] zoo_1.8-12                  SummarizedExperiment_1.28.0
 [69] cluster_2.1.4               magrittr_2.0.3             
 [71] data.table_1.14.8           scattermore_0.8            
 [73] lmtest_0.9-40               RANN_2.6.1                 
 [75] ProtGenerics_1.30.0         fitdistrplus_1.1-8         
 [77] matrixStats_0.63.0          hms_1.1.3                  
 [79] patchwork_1.1.2             mime_0.12                  
 [81] evaluate_0.20               xtable_1.8-4               
 [83] XML_3.99-0.14               gridExtra_2.3              
 [85] compiler_4.2.3              biomaRt_2.54.0             
 [87] KernSmooth_2.23-20          crayon_1.5.2               
 [89] htmltools_0.5.5             later_1.3.0                
 [91] tidyr_1.3.0                 DBI_1.1.3                  
 [93] tweenr_2.0.2                dbplyr_2.3.2               
 [95] MASS_7.3-58.3               rappdirs_0.3.3             
 [97] Matrix_1.5-4                cli_3.6.1                  
 [99] parallel_4.2.3              igraph_1.4.2               
[101] pkgconfig_2.0.3             GenomicAlignments_1.34.0   
[103] sp_1.6-0                    IRdisplay_1.1              
[105] plotly_4.10.1               spatstat.sparse_3.0-1      
[107] xml2_1.3.3                  XVector_0.38.0             
[109] stringr_1.5.0               digest_0.6.31              
[111] RcppAnnoy_0.0.20            spatstat.data_3.0-1        
[113] Biostrings_2.66.0           leiden_0.4.3               
[115] fastmatch_1.1-3             uwot_0.1.14                
[117] restfulr_0.0.15             curl_4.3.3                 
[119] shiny_1.7.4                 Rsamtools_2.14.0           
[121] rjson_0.2.21                lifecycle_1.0.3            
[123] nlme_3.1-162                jsonlite_1.8.4             
[125] viridisLite_0.4.1           fansi_1.0.4                
[127] pillar_1.9.0                lattice_0.21-8             
[129] KEGGREST_1.38.0             fastmap_1.1.1              
[131] httr_1.4.5                  survival_3.5-5             
[133] glue_1.6.2                  png_0.1-8                  
[135] bit_4.0.5                   stringi_1.7.12             
[137] blob_1.2.4                  memoise_2.0.1              
[139] IRkernel_1.3.2              irlba_2.3.5.1              
[141] future.apply_1.10.0        
In [12]:
brain.exp1.rna <- readRDS(file = "./01.MouseBrainExp1/merge_mtx/brain.exp1.rna.object.rds")
brain.exp2.rna <- readRDS(file = "./02.MouseBrainExp2/merge_mtx/brain.exp2.rna.object.rds")
brain.exp3.rna <- readRDS(file = "./03.MouseBrainExp3/merge_mtx/brain.exp3.rna.object.rds")
brain.exp4.rna <- readRDS(file = "./04.MouseBrainExp4/merge_mtx/brain.exp4.rna.object.rds")
brain.exp5.rna <- readRDS(file = "./05.MouseBrainExp5/merge_mtx/brain.exp5.rna.object.rds")
brain.exp6.rna <- readRDS(file = "./06.MouseBrainExp6/merge_mtx/brain.exp6.rna.object.rds")
brain.exp7.rna <- readRDS(file = "./07.MouseBrainExp7/merge_mtx/brain.exp7.rna.object.rds")
brain.exp8.rna <- readRDS(file = "./08.MouseBrainExp8/merge_mtx/brain.exp8.rna.object.rds")
brain.exp9.rna <- readRDS(file = "./09.MouseBrainExp9/merge_mtx/brain.exp9.rna.object.rds")
brain.exp10.rna <- readRDS(file = "./10.MouseBrainExp10/merge_mtx/brain.exp10.rna.object.rds")
brain.exp11.rna <- readRDS(file = "./11.MouseBrainExp11/merge_mtx/brain.exp11.rna.object.rds")
brain.exp12.rna <- readRDS(file = "./12.MouseBrainExp12/merge_mtx/brain.exp12.rna.object.rds")
brain.exp13.rna <- readRDS(file = "./13.MouseBrainExp13/merge_mtx/brain.exp13.rna.object.rds")
brain.exp14.rna <- readRDS(file = "./14.MouseBrainExp14/merge_mtx/brain.exp14.rna.object.rds")
In [13]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp10.rna'
  3. 'brain.exp11.rna'
  4. 'brain.exp12.rna'
  5. 'brain.exp13.rna'
  6. 'brain.exp14.rna'
  7. 'brain.exp2.rna'
  8. 'brain.exp3.rna'
  9. 'brain.exp4.rna'
  10. 'brain.exp5.rna'
  11. 'brain.exp6.rna'
  12. 'brain.exp7.rna'
  13. 'brain.exp8.rna'
  14. 'brain.exp9.rna'
In [15]:
options(Seurat.object.assay.version = 'v5')
options(future.globals.maxSize = 6e9)
In [16]:
brain.exp1.rna.v5<-UpdateSeuratObject(brain.exp1.rna)
Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

In [18]:
brain.exp2.rna.v5<-UpdateSeuratObject(brain.exp2.rna)
brain.exp3.rna.v5<-UpdateSeuratObject(brain.exp3.rna)
brain.exp4.rna.v5<-UpdateSeuratObject(brain.exp4.rna)
brain.exp5.rna.v5<-UpdateSeuratObject(brain.exp5.rna)
brain.exp6.rna.v5<-UpdateSeuratObject(brain.exp6.rna)
brain.exp7.rna.v5<-UpdateSeuratObject(brain.exp7.rna)
brain.exp8.rna.v5<-UpdateSeuratObject(brain.exp8.rna)
brain.exp9.rna.v5<-UpdateSeuratObject(brain.exp9.rna)
brain.exp10.rna.v5<-UpdateSeuratObject(brain.exp10.rna)
brain.exp11.rna.v5<-UpdateSeuratObject(brain.exp11.rna)
brain.exp12.rna.v5<-UpdateSeuratObject(brain.exp12.rna)
brain.exp13.rna.v5<-UpdateSeuratObject(brain.exp13.rna)
brain.exp14.rna.v5<-UpdateSeuratObject(brain.exp14.rna)
Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

Validating object structure

Updating object slots

Ensuring keys are in the proper structure

Ensuring keys are in the proper structure

Ensuring feature names don't have underscores or pipes

Updating slots in RNA

Validating object structure for Assay ‘RNA’

Object representation is consistent with the most current Seurat version

In [19]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp1.rna.v5'
  3. 'brain.exp10.rna'
  4. 'brain.exp10.rna.v5'
  5. 'brain.exp11.rna'
  6. 'brain.exp11.rna.v5'
  7. 'brain.exp12.rna'
  8. 'brain.exp12.rna.v5'
  9. 'brain.exp13.rna'
  10. 'brain.exp13.rna.v5'
  11. 'brain.exp14.rna'
  12. 'brain.exp14.rna.v5'
  13. 'brain.exp2.rna'
  14. 'brain.exp2.rna.v5'
  15. 'brain.exp3.rna'
  16. 'brain.exp3.rna.v5'
  17. 'brain.exp4.rna'
  18. 'brain.exp4.rna.v5'
  19. 'brain.exp5.rna'
  20. 'brain.exp5.rna.v5'
  21. 'brain.exp6.rna'
  22. 'brain.exp6.rna.v5'
  23. 'brain.exp7.rna'
  24. 'brain.exp7.rna.v5'
  25. 'brain.exp8.rna'
  26. 'brain.exp8.rna.v5'
  27. 'brain.exp9.rna'
  28. 'brain.exp9.rna.v5'
In [20]:
rm(brain.exp1.rna, brain.exp2.rna, brain.exp3.rna, brain.exp4.rna, brain.exp5.rna, brain.exp6.rna, brain.exp7.rna, 
   brain.exp8.rna, brain.exp9.rna, brain.exp10.rna, brain.exp11.rna, brain.exp12.rna, brain.exp13.rna, brain.exp14.rna)
In [21]:
ls()
  1. 'brain.exp1.rna.v5'
  2. 'brain.exp10.rna.v5'
  3. 'brain.exp11.rna.v5'
  4. 'brain.exp12.rna.v5'
  5. 'brain.exp13.rna.v5'
  6. 'brain.exp14.rna.v5'
  7. 'brain.exp2.rna.v5'
  8. 'brain.exp3.rna.v5'
  9. 'brain.exp4.rna.v5'
  10. 'brain.exp5.rna.v5'
  11. 'brain.exp6.rna.v5'
  12. 'brain.exp7.rna.v5'
  13. 'brain.exp8.rna.v5'
  14. 'brain.exp9.rna.v5'
In [22]:
brain.exp1.rna.v5[["sex"]] <- "Female"
brain.exp2.rna.v5[["sex"]] <- "Female"
brain.exp3.rna.v5[["sex"]] <- "Female"
brain.exp4.rna.v5[["sex"]] <- "Female"
brain.exp5.rna.v5[["sex"]] <- "Male"
brain.exp6.rna.v5[["sex"]] <- "Male"
brain.exp7.rna.v5[["sex"]] <- "Male"
brain.exp8.rna.v5[["sex"]] <- "Female"
brain.exp9.rna.v5[["sex"]] <- "Female"
brain.exp10.rna.v5[["sex"]] <- "Male"
brain.exp11.rna.v5[["sex"]] <- "Female"
brain.exp12.rna.v5[["sex"]] <- "Female"
brain.exp13.rna.v5[["sex"]] <- "Male"
In [23]:
brain.exp1.rna.v5[["rep"]] <- "FemaleB"
brain.exp2.rna.v5[["rep"]] <- "FemaleB"
brain.exp3.rna.v5[["rep"]] <- "FemaleB"
brain.exp4.rna.v5[["rep"]] <- "FemaleB"
brain.exp5.rna.v5[["rep"]] <- "MaleA"
brain.exp6.rna.v5[["rep"]] <- "MaleA"
brain.exp7.rna.v5[["rep"]] <- "MaleB"
brain.exp8.rna.v5[["rep"]] <- "FemaleA"
brain.exp9.rna.v5[["rep"]] <- "FemaleA"
brain.exp10.rna.v5[["rep"]] <- "MaleB"
brain.exp11.rna.v5[["rep"]] <- "FemaleA"
brain.exp12.rna.v5[["rep"]] <- "FemaleA"
In [24]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain.exp13.rna.v5@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
 1860  1511  2472  2717  4810  6075  6227  8602  4377  5036  1430  1708  6930 
   14    15    16    17    18    19    20    21    22    23 
12814  8189  5452  9087 12146  4454  3429  4516  5801     1 
In [25]:
replicate <- rep("MaleA",dim(bc)[1])
replicate[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="12"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="22"]<-"MaleB"
replicate[bc[,4]=="06"|bc[,4]=="08"|bc[,4]=="10"]<-"MaleB"
replicate[bc[,4]=="18"|bc[,4]=="20"]<-"MaleB"
table(replicate)
replicate
MaleA MaleB 
58017 61627 
In [26]:
brain.exp13.rna.v5@meta.data<-cbind(brain.exp13.rna.v5@meta.data, cbind(replicate))
In [27]:
ls()
  1. 'bc'
  2. 'brain.exp1.rna.v5'
  3. 'brain.exp10.rna.v5'
  4. 'brain.exp11.rna.v5'
  5. 'brain.exp12.rna.v5'
  6. 'brain.exp13.rna.v5'
  7. 'brain.exp14.rna.v5'
  8. 'brain.exp2.rna.v5'
  9. 'brain.exp3.rna.v5'
  10. 'brain.exp4.rna.v5'
  11. 'brain.exp5.rna.v5'
  12. 'brain.exp6.rna.v5'
  13. 'brain.exp7.rna.v5'
  14. 'brain.exp8.rna.v5'
  15. 'brain.exp9.rna.v5'
  16. 'replicate'
In [36]:
table(brain.exp1.rna@meta.data$brainregion,brain.exp1.rna@meta.data$modality)
     
      H3K27ac H3K27me3
  CPU    4314     9673
  HCa    4223     8256
  HCp    2177     5902
  HYP    2812     5765
In [37]:
table(brain.exp2.rna@meta.data$brainregion,brain.exp2.rna@meta.data$modality)
     
      H3K4me1 H3K9me3
  CPU   14671   15513
  HCa    6976   10995
  HCp    9550    9008
  HYP    7913   10507
In [38]:
table(brain.exp3.rna@meta.data$brainregion,brain.exp3.rna@meta.data$modality)
         
          H3K27ac H3K27me3
  AMY        2312     9119
  ERC        3194    11305
  mESC       9557     1793
  NAC        1131     2502
  PFC         927     5423
  VTA_SnR    3582     5641
In [39]:
table(brain.exp4.rna@meta.data$brainregion,brain.exp4.rna@meta.data$modality)
         
          H3K4me1 H3K9me3
  AMY        5939    6267
  ERC        7562    8772
  NAC       10066    9517
  PFC        2333     860
  VTA_SnR    6559    7190
In [40]:
table(brain.exp5.rna@meta.data$brainregion,brain.exp5.rna@meta.data$modality)
           
            H3K27ac H3K27me3
  AMY          5117     7680
  CPU         12033    21265
  ERC          8944    12224
  HCa          7772    15352
  HCp          9541    13395
  HYP          9109    14363
  ITGremove       8        0
  NAC          6048    12099
  VTA_SnR      3952     5140
In [41]:
table(brain.exp6.rna@meta.data$brainregion,brain.exp6.rna@meta.data$modality)
         
          H3K4me1 H3K9me3
  AMY        3031    1667
  CPU        4131    3041
  ERC        4686    1888
  HCa        2364    3463
  HCp        4215    4379
  HYP        1994    2677
  NAC        7315    2777
  VTA_SnR    1840    1072
In [42]:
table(brain.exp7.rna@meta.data$brainregion,brain.exp7.rna@meta.data$modality)
         
          H3K27ac H3K27me3
  AMY        3769     8323
  CPU        7318    17765
  ERC        8987    11710
  HCa        2214     3788
  HCp        4206     8506
  HYP        5010    11086
  NAC        3411     3691
  VTA_SnR    3665     3864
In [43]:
table(brain.exp8.rna@meta.data$brainregion,brain.exp8.rna@meta.data$modality)
     
      H3K27ac H3K27me3
  CPU   10469    21741
  HCa    6868    19722
  HCp    6017    13973
  HYP    7050    13918
In [44]:
table(brain.exp9.rna@meta.data$brainregion,brain.exp9.rna@meta.data$modality)
         
          H3K4me1 H3K9me3
  AMY       13227   14400
  ERC       17469   15466
  NAC       10856   15420
  VTA_SnR    7570    7746
In [45]:
table(brain.exp10.rna@meta.data$brainregion,brain.exp10.rna@meta.data$modality)
         
          H3K4me1 H3K9me3
  AMY       19934   17938
  CPU       24646   25417
  ERC        8429    8980
  HCa       16622   19715
  HCp       16583   16009
  HYP       22058   21110
  NAC       22724   21051
  VTA_SnR   15664    9805
In [46]:
table(brain.exp11.rna@meta.data$brainregion,brain.exp11.rna@meta.data$modality)
         
          H3K27ac H3K27me3
  AMY       20095    10047
  ERC       19709    24213
  NAC        3824    11754
  PFC        8654    21720
  VTA_SnR    6760     6156
In [47]:
table(brain.exp12.rna@meta.data$brainregion,brain.exp12.rna@meta.data$modality)
     
      H3K4me1 H3K9me3
  CPU   22351   24734
  HCa    7838    8930
  HCp   17925   17676
  HYP   19857    8027
  PFC    3686    2350
In [50]:
table(brain.exp13.rna@meta.data$oriBarcode,brain.exp13.rna@meta.data$modality)
    
     H3K27ac H3K27me3
  01    1860        0
  02    1511        0
  03    2472        0
  04    2717        0
  05    4810        0
  06    6075        0
  07    6227        0
  08    8602        0
  09    4377        0
  10    5036        0
  11    1430        0
  12    1708        0
  13       0     6930
  14       0    12814
  15       0     8189
  16    5452        0
  17       0     9087
  18       0    12146
  19       0     4454
  20       0     3429
  21       0     4516
  22       0     5801
  23       1        0
In [28]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain.exp13.rna.v5@assays$RNA@data)),split=":")))
table(bc[,4])
   01    02    03    04    05    06    07    08    09    10    11    12    13 
 1860  1511  2472  2717  4810  6075  6227  8602  4377  5036  1430  1708  6930 
   14    15    16    17    18    19    20    21    22    23 
12814  8189  5452  9087 12146  4454  3429  4516  5801     1 
In [29]:
brain.exp13.rna.v5[["modality"]] <- NULL
In [30]:
modality <- rep("H3K27ac",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"]<-"H3K27me3"

table(modality)
modality
 H3K27ac H3K27me3 
   46826    72818 
In [31]:
brain.exp13.rna.v5@meta.data<-cbind(brain.exp13.rna.v5@meta.data, cbind(modality))
In [32]:
table(brain.exp13.rna.v5@meta.data$oriBarcode,brain.exp13.rna.v5@meta.data$modality)
    
     H3K27ac H3K27me3
  01    1860        0
  02    1511        0
  03    2472        0
  04    2717        0
  05    4810        0
  06    6075        0
  07    6227        0
  08    8602        0
  09    4377        0
  10    5036        0
  11    1430        0
  12    1708        0
  13       0     6930
  14       0    12814
  15       0     8189
  16       0     5452
  17       0     9087
  18       0    12146
  19       0     4454
  20       0     3429
  21       0     4516
  22       0     5801
  23       1        0
In [33]:
replicate <- rep("MaleA",dim(bc)[1])
replicate[bc[,4]=="03"|bc[,4]=="04"|bc[,4]=="05"|bc[,4]=="06"|bc[,4]=="08"|bc[,4]=="10"|bc[,4]=="12"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="20"|bc[,4]=="22"]<-"MaleB"

table(replicate)
replicate
MaleA MaleB 
44120 75524 
In [34]:
brain.exp13.rna.v5[["replicate"]] <- NULL
In [35]:
brain.exp13.rna.v5@meta.data<-cbind(brain.exp13.rna.v5@meta.data, cbind(replicate))
In [36]:
table(brain.exp13.rna.v5@meta.data$replicate,brain.exp13.rna.v5@meta.data$brainregion)
       
          HCa   HCp   PFC remove VTA_SnR
  MaleA     0  6227 29061      1    8831
  MaleB 32118  8602 26339      0    8465
In [27]:
table(brain.exp13.rna.v5@meta.data$brainregion,brain.exp13.rna.v5@meta.data$modality)
         
          H3K27ac H3K27me3
  HCa       10885    21233
  HCp       14829        0
  PFC       11698    43702
  remove        1        0
  VTA_SnR    9413     7883
In [37]:
table(brain.exp14.rna.v5@meta.data$oriBarcode,brain.exp14.rna.v5@meta.data$modality)
    
     H3K4me1 H3K9me3
  01    1286       0
  02    1726       0
  03    4797       0
  04    3008       0
  05    2443       0
  06    3354       0
  07    2532       0
  08    3720       0
  09    3697       0
  10    3785       0
  11    5640       0
  12    4732       0
  13       0    4340
  14       0    3130
  15       0    3979
  16    3305       0
  17       0    5283
  18       0    5916
  19       0    3486
  20       0    2758
  21       0    6647
  22       0    6528
  23       0    4686
  24       0    5899
In [38]:
bc<-matrix(ncol=4, byrow = T, unlist(strsplit(as.character(colnames(brain.exp14.rna.v5@assays$RNA@data)),split=":")))
table(bc[,4])
  01   02   03   04   05   06   07   08   09   10   11   12   13   14   15   16 
1286 1726 4797 3008 2443 3354 2532 3720 3697 3785 5640 4732 4340 3130 3979 3305 
  17   18   19   20   21   22   23   24 
5283 5916 3486 2758 6647 6528 4686 5899 
In [39]:
brain.exp14.rna.v5[["modality"]] <- NULL
In [40]:
modality <- rep("H3K4me1",dim(bc)[1])
modality[bc[,4]=="13"|bc[,4]=="14"|bc[,4]=="15"|bc[,4]=="16"|bc[,4]=="17"|bc[,4]=="18"|bc[,4]=="19"|bc[,4]=="20"|bc[,4]=="21"|bc[,4]=="22"|bc[,4]=="23"|bc[,4]=="24"]<-"H3K9me3"

table(modality)
modality
H3K4me1 H3K9me3 
  40720   55957 
In [41]:
brain.exp14.rna.v5@meta.data<-cbind(brain.exp14.rna.v5@meta.data, cbind(modality))
In [42]:
table(brain.exp14.rna.v5@meta.data$oriBarcode,brain.exp14.rna.v5@meta.data$modality)
    
     H3K4me1 H3K9me3
  01    1286       0
  02    1726       0
  03    4797       0
  04    3008       0
  05    2443       0
  06    3354       0
  07    2532       0
  08    3720       0
  09    3697       0
  10    3785       0
  11    5640       0
  12    4732       0
  13       0    4340
  14       0    3130
  15       0    3979
  16       0    3305
  17       0    5283
  18       0    5916
  19       0    3486
  20       0    2758
  21       0    6647
  22       0    6528
  23       0    4686
  24       0    5899
In [43]:
replicate <- rep("MaleA",dim(bc)[1])
replicate[bc[,4]=="02"|bc[,4]=="14"]<-"MaleB"
replicate[bc[,4]=="11"|bc[,4]=="21"]<-"FemaleA"
replicate[bc[,4]=="12"|bc[,4]=="22"]<-"FemaleB"
table(replicate)
replicate
FemaleA FemaleB   MaleA   MaleB 
  12287   11260   68274    4856 
In [44]:
brain.exp14.rna.v5@meta.data<-cbind(brain.exp14.rna.v5@meta.data, cbind(replicate))
In [45]:
table(brain.exp14.rna.v5@meta.data$oriBarcode,brain.exp14.rna.v5@meta.data$replicate)
    
     FemaleA FemaleB MaleA MaleB
  01       0       0  1286     0
  02       0       0     0  1726
  03       0       0  4797     0
  04       0       0  3008     0
  05       0       0  2443     0
  06       0       0  3354     0
  07       0       0  2532     0
  08       0       0  3720     0
  09       0       0  3697     0
  10       0       0  3785     0
  11    5640       0     0     0
  12       0    4732     0     0
  13       0       0  4340     0
  14       0       0     0  3130
  15       0       0  3979     0
  16       0       0  3305     0
  17       0       0  5283     0
  18       0       0  5916     0
  19       0       0  3486     0
  20       0       0  2758     0
  21    6647       0     0     0
  22       0    6528     0     0
  23       0       0  4686     0
  24       0       0  5899     0
In [46]:
table(brain.exp14.rna.v5@meta.data$brainregion,brain.exp14.rna.v5@meta.data$modality)
         
          H3K4me1 H3K9me3
  AMY           0   11199
  ERC        5797    7284
  HCa       18177   13175
  HYP        6252       0
  PFC        3012   18055
  VTA_SnR    7482    6244
In [47]:
Idents(brain.exp3.rna.v5) <- "brainregion"
In [48]:
brain.exp3.rna.new <- subset(brain.exp3.rna.v5, idents = c("mESC"), invert = TRUE)
In [40]:
table(brain.exp3.rna.new@meta.data$brainregion,brain.exp3.rna.new@meta.data$modality)
         
          H3K27ac H3K27me3
  AMY        2312     9119
  ERC        3194    11305
  NAC        1131     2502
  PFC         927     5423
  VTA_SnR    3582     5641
In [49]:
brain.exp3.rna.v5 <- brain.exp3.rna.new
In [50]:
rm(brain.exp3.rna.new)
In [ ]:
saveRDS(brain.exp3.rna, file = "03.MouseBrainExp3/merge_mtx/brain.exp3.rna.object_new.rds")
In [51]:
Idents(brain.exp5.rna.v5) <- "brainregion"
In [52]:
brain.exp5.rna.new <- subset(brain.exp5.rna.v5, idents = c("ITGremove"), invert = TRUE)
In [53]:
table(brain.exp5.rna.new@meta.data$brainregion,brain.exp5.rna.new@meta.data$modality)
         
          H3K27ac H3K27me3
  AMY        5117     7680
  CPU       12033    21265
  ERC        8944    12224
  HCa        7772    15352
  HCp        9541    13395
  HYP        9109    14363
  NAC        6048    12099
  VTA_SnR    3952     5140
In [54]:
brain.exp5.rna.v5 <- brain.exp5.rna.new
In [55]:
rm(brain.exp5.rna.new)
In [ ]:
saveRDS(brain.exp5.rna, file = "05.MouseBrainExp5/merge_mtx/brain.exp5.rna.object_new.rds")
In [56]:
Idents(brain.exp13.rna.v5) <- "brainregion"
In [58]:
brain.exp13.rna.new <- subset(brain.exp13.rna.v5, idents = c("remove"), invert = TRUE)
In [59]:
table(brain.exp13.rna.new@meta.data$brainregion,brain.exp13.rna.new@meta.data$modality)
         
          H3K27ac H3K27me3
  HCa       10885    21233
  HCp       14829        0
  PFC       11698    43702
  VTA_SnR    9413     7883
In [60]:
brain.exp13.rna.v5 <- brain.exp13.rna.new
In [61]:
rm(brain.exp13.rna.new)
In [ ]:
saveRDS(brain.exp13.rna, file = "13.MouseBrainExp13/merge_mtx/brain.exp13.rna.object_new.rds")
In [62]:
rm(bc,modality,replicate)
In [63]:
brain.exp1.rna.v5[["exp"]] <- "Exp1"
brain.exp2.rna.v5[["exp"]] <- "Exp2"
brain.exp3.rna.v5[["exp"]] <- "Exp3"
brain.exp4.rna.v5[["exp"]] <- "Exp4"
brain.exp5.rna.v5[["exp"]] <- "Exp5"
brain.exp6.rna.v5[["exp"]] <- "Exp6"
brain.exp7.rna.v5[["exp"]] <- "Exp7"
brain.exp8.rna.v5[["exp"]] <- "Exp8"
brain.exp9.rna.v5[["exp"]] <- "Exp9"
brain.exp10.rna.v5[["exp"]] <- "Exp10"
brain.exp11.rna.v5[["exp"]] <- "Exp11"
brain.exp12.rna.v5[["exp"]] <- "Exp12"
brain.exp13.rna.v5[["exp"]] <- "Exp13"
brain.exp14.rna.v5[["exp"]] <- "Exp14"
In [65]:
getwd()
'/projects/ps-renlab2/zhw063/99.MouseBrainPairedTag'
In [66]:
saveRDS(brain.exp1.rna.v5, file = "./seurat_v5_objects/brain.exp1.rna.object.rds")
saveRDS(brain.exp2.rna.v5, file = "./seurat_v5_objects/brain.exp2.rna.object.rds")
saveRDS(brain.exp3.rna.v5, file = "./seurat_v5_objects/brain.exp3.rna.object.rds")
saveRDS(brain.exp4.rna.v5, file = "./seurat_v5_objects/brain.exp4.rna.object.rds")
saveRDS(brain.exp5.rna.v5, file = "./seurat_v5_objects/brain.exp5.rna.object.rds")
saveRDS(brain.exp6.rna.v5, file = "./seurat_v5_objects/brain.exp6.rna.object.rds")
saveRDS(brain.exp7.rna.v5, file = "./seurat_v5_objects/brain.exp7.rna.object.rds")
saveRDS(brain.exp8.rna.v5, file = "./seurat_v5_objects/brain.exp8.rna.object.rds")
saveRDS(brain.exp9.rna.v5, file = "./seurat_v5_objects/brain.exp9.rna.object.rds")
saveRDS(brain.exp10.rna.v5, file = "./seurat_v5_objects/brain.exp10.rna.object.rds")
saveRDS(brain.exp11.rna.v5, file = "./seurat_v5_objects/brain.exp11.rna.object.rds")
saveRDS(brain.exp12.rna.v5, file = "./seurat_v5_objects/brain.exp12.rna.object.rds")
saveRDS(brain.exp13.rna.v5, file = "./seurat_v5_objects/brain.exp13.rna.object.rds")
saveRDS(brain.exp14.rna.v5, file = "./seurat_v5_objects/brain.exp14.rna.object.rds")
In [67]:
brain <- merge(brain.exp1.rna.v5, y=c(brain.exp2.rna.v5, brain.exp3.rna.v5, brain.exp4.rna.v5, brain.exp5.rna.v5, 
                                   brain.exp6.rna.v5, brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, 
                                   brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain")
Error in eval(expr, envir, enclos): vector::reserve
Traceback:

1. merge(brain.exp1.rna.v5, y = c(brain.exp2.rna.v5, brain.exp3.rna.v5, 
 .     brain.exp4.rna.v5, brain.exp5.rna.v5, brain.exp6.rna.v5, 
 .     brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, 
 .     brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, 
 .     brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain")
2. merge(brain.exp1.rna.v5, y = c(brain.exp2.rna.v5, brain.exp3.rna.v5, 
 .     brain.exp4.rna.v5, brain.exp5.rna.v5, brain.exp6.rna.v5, 
 .     brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, 
 .     brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, 
 .     brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain")
3. merge.Seurat(brain.exp1.rna.v5, y = c(brain.exp2.rna.v5, brain.exp3.rna.v5, 
 .     brain.exp4.rna.v5, brain.exp5.rna.v5, brain.exp6.rna.v5, 
 .     brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, 
 .     brain.exp10.rna.v5, brain.exp11.rna.v5, brain.exp12.rna.v5, 
 .     brain.exp13.rna.v5, brain.exp14.rna.v5), project = "brain")
4. merge(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y], 
 .     FUN = "[[", assay), labels = projects, add.cell.ids = NULL, 
 .     collapse = collapse, merge.data = merge.data)
5. merge.Assay(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y], 
 .     FUN = "[[", assay), labels = projects, add.cell.ids = NULL, 
 .     collapse = collapse, merge.data = merge.data)
6. RowMergeSparseMatrices(mat1 = counts.mats[[1]], mat2 = counts.mats[2:length(x = counts.mats)])
7. RowMergeMatricesList(mat_list = all.mat, mat_rownames = all.rownames, 
 .     all_rownames = all.names)
In [68]:
library(scCustomize)
object_list <- list(brain.exp1.rna.v5,brain.exp2.rna.v5, brain.exp3.rna.v5, brain.exp4.rna.v5, brain.exp5.rna.v5,
                    brain.exp6.rna.v5, brain.exp7.rna.v5, brain.exp8.rna.v5, brain.exp9.rna.v5, brain.exp10.rna.v5, 
                    brain.exp11.rna.v5, brain.exp12.rna.v5, brain.exp13.rna.v5, brain.exp14.rna.v5)
brain <- Merge_Seurat_List(list_seurat = object_list)
scCustomize v2.0.0
If you find the scCustomize useful please cite.
See 'samuel-marsh.github.io/scCustomize/articles/FAQ.html' for citation info.

Error in eval(expr, envir, enclos): std::bad_alloc
Traceback:

1. Merge_Seurat_List(list_seurat = object_list)
2. reduce(list_seurat, function(x, y) {
 .     merge(x = x, y = y, merge.data = merge.data, project = project)
 . })
3. reduce_impl(.x, .f, ..., .init = .init, .dir = .dir)
4. fn(out, elt, ...)
5. merge(x = x, y = y, merge.data = merge.data, project = project)
6. merge.Seurat(x = x, y = y, merge.data = merge.data, project = project)
7. merge(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y], 
 .     FUN = "[[", assay), labels = projects, add.cell.ids = NULL, 
 .     collapse = collapse, merge.data = merge.data)
8. merge.Assay(x = objects[[idx.x]][[assay]], y = lapply(X = objects[idx.y], 
 .     FUN = "[[", assay), labels = projects, add.cell.ids = NULL, 
 .     collapse = collapse, merge.data = merge.data)
9. RowMergeSparseMatrices(mat1 = data.mats[[1]], mat2 = data.mats[2:length(x = data.mats)])
10. RowMergeMatricesList(mat_list = all.mat, mat_rownames = all.rownames, 
  .     all_rownames = all.names)

install.packages("scCustomize")

In [58]:
ls()
  1. 'brain.exp1.rna'
  2. 'brain.exp10.rna'
  3. 'brain.exp11.rna'
  4. 'brain.exp12.rna'
  5. 'brain.exp13.rna'
  6. 'brain.exp14.rna'
  7. 'brain.exp2.rna'
  8. 'brain.exp3.rna'
  9. 'brain.exp4.rna'
  10. 'brain.exp5.rna'
  11. 'brain.exp6.rna'
  12. 'brain.exp7.rna'
  13. 'brain.exp8.rna'
  14. 'brain.exp9.rna'